Intelligent control system and control method of cell activation instrument

Abstract

This invention relates to the field of cell activator technology and discloses an intelligent control method for a cell activator. The method includes: constructing a data acquisition module for the cell activator to acquire weak magnetic field signals from a target user; performing spectral analysis on the weak magnetic field signals to analyze the cross-linking patterns of biomolecules and define the cross-linking wave equations of biomolecules; fitting a biological wave digital model of the target user and analyzing the harmonic coefficients of the origin frequency and the moving point frequency; when the harmonic coefficients do not meet a preset harmonic coefficient threshold, defining the target user's corresponding physiological state as a sub-healthy state and identifying abnormal biomolecules in the target user; determining the magnetic field strength and frequency of the cell activator, converting the corresponding magnetic field of the cell activator into terahertz magnetic energy to correct abnormal biomolecules; and defining the target user's corresponding physiological state as a healthy state when the normal harmonic coefficients meet the harmonic coefficient threshold. This invention can improve the precise control of cell enhancement effects in cell activators.

Description

Technical Field

[0001] This invention relates to the field of cell activation technology, and more particularly to an intelligent control system for a cell activation instrument. Background Technology

[0002] A cell activation instrument is a laboratory device used to activate cells through various physical, chemical, or biological methods to promote cell function, proliferation, differentiation, or other biological responses. By activating specific cells, cell activation instruments promote the repair and regeneration of damaged tissues, thereby reducing unnecessary cell damage and side effects and improving treatment safety.

[0003] Currently, traditional cell revitalization devices use a simple combination of heat and magnetic therapy. This method generates a lot of heat during the cell activation process, resulting in poor cell enhancement and poor heat dissipation. Summary of the Invention

[0004] This invention provides an intelligent control system for a cell activator, the main purpose of which is to improve the precision control of the cell activator to enhance cell enhancement effects.

[0005] To achieve the above objectives, the present invention provides an intelligent control system for a cell activation instrument, comprising: a data acquisition and analysis module, a harmony coefficient calculation module, an abnormal molecule identification module, an abnormal molecule correction module, and a health status definition module;

[0006] The data acquisition and analysis module is used to construct the data acquisition module of the cell activation instrument. Based on the data acquisition module, it acquires the weak magnetic field signal of the target user, performs spectral analysis on the weak magnetic field signal to obtain cell frequency information, identifies the life macromolecules of the target user, analyzes the cross-linking mode of the life macromolecules, and defines the cross-linking wave equation of the life macromolecules according to the cross-linking mode.

[0007] The harmonic coefficient calculation module is used to fit the target user's biological wave digital model based on the cell frequency information and the cross-linking wave equation, analyze the origin life state and dynamic life state of the biomolecules based on the biological wave digital model, calculate the origin frequency and dynamic frequency of the origin life state and the dynamic life state, and analyze the harmonic coefficient of the origin frequency and the dynamic frequency.

[0008] The abnormal molecule identification module is used to define the physiological state of the target user as a sub-healthy state when the harmonic coefficient does not meet the preset harmonic coefficient threshold, and to identify the abnormal biomolecules of the target user based on the origin frequency and the movement frequency.

[0009] The abnormal molecule correction module is used to determine the magnetic field strength and frequency of the cell activator based on the abnormal biomolecules and the sub-health state; based on the magnetic field strength and frequency, the corresponding magnetic field of the cell activator is converted into terahertz magnetic energy; the terahertz magnetic energy is used to induce cell resonance in the target user; based on the cell resonance, the cell activity of the target cell corresponding to the target user is stimulated; based on the cell activity, the biological information released by the target cell is analyzed; and based on the biological information, the abnormal biomolecules are corrected to obtain normal biomolecules.

[0010] The health status definition module is used to calculate the normal origin frequency and normal dynamic frequency of the normal biomolecules, analyze the normal harmonic coefficients of the normal origin frequency and the normal dynamic frequency, and define the physiological state of the target user as a healthy state when the normal harmonic coefficients meet the harmonic coefficient threshold.

[0011] Optionally, the data acquisition module for constructing the cell activation instrument includes:

[0012] Construct the data acquisition framework for the cell activation instrument;

[0013] The data acquisition framework is analyzed to determine the types of signals it needs to acquire and the acquisition performance parameters, which include: acquisition rate, resolution, and dynamic range.

[0014] Based on the signal type and the acquisition performance parameters, construct the sensor network of the data acquisition framework;

[0015] The signal processing circuit of the data acquisition framework is defined, wherein the signal processing circuit includes: a signal amplification circuit, a signal filtering circuit, and a signal conditioning circuit;

[0016] Define the analog-to-digital conversion algorithm of the data acquisition framework, and construct the signal processing module of the data acquisition framework based on the analog-to-digital conversion algorithm;

[0017] The signal processing module, the signal processing circuit, and the sensor network are integrated into the data acquisition framework to obtain the data acquisition module of the cell activation instrument.

[0018] Optionally, the step of performing spectral analysis on the weak magnetic field signal to obtain cell frequency information includes:

[0019] The weak magnetic field signal is preprocessed to obtain a preprocessed magnetic field signal;

[0020] The preprocessed magnetic field signal is converted from analog to digital into a magnetic field digital signal.

[0021] Applying a Fast Fourier Transform to the digital magnetic field signal yields a frequency domain signal;

[0022] Perform spectral analysis on the frequency domain signal to obtain the signal spectrum and identify the characteristic peaks of the signal spectrum;

[0023] Based on the characteristic peaks, cell frequency information is extracted from the signal spectrum.

[0024] This invention, through its data acquisition module, enables real-time signal processing and analysis of weak magnetic field signals from the target user, allowing for non-invasive monitoring of physiological parameters and reducing the risk of unsuitability for the target user. Optionally, by defining the cross-linking wave equation of the biomolecules based on the cross-linking method, this invention can better understand how quantum effects influence the cross-linking of biomolecules, thus providing a theoretical basis for quantum biowave modulation. Furthermore, by analyzing the harmonic coefficients of the origin frequency and the moving frequency, this invention can determine the response of a biological system to external stimuli or internal changes, thereby identifying abnormal changes in the biological system. This invention, through defining the target user's physiological state as sub-healthy when the harmony coefficient does not meet a preset harmony coefficient threshold, can identify potential sub-healthy states before obvious abnormalities appear, thus providing quantum spiral bio-wave conditioning. Furthermore, by analyzing the biological information released by the target cells based on cell activity, this invention can enhance signal transmission between quantum bio-waves and cells, improving the conditioning effect. Finally, by calculating the normal origin frequency and normal dynamic frequency of normal biomolecules, this invention can analyze the parameters of biomolecules after conditioning, thereby analyzing the conditioning effect. Therefore, this invention can improve the precise control of cell enhancement effects in cell activation instruments. Attached Figure Description

[0025] Figure 1 A functional block diagram of an intelligent control system for a cell activation instrument provided in an embodiment of the present invention;

[0026] Figure 2 This is a flowchart illustrating an intelligent control method for a cell activation instrument according to an embodiment of the present invention.

[0027] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0029] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0030] In practice, the server-side equipment deployed in the intelligent control system of the cell activator may consist of one or more devices. The aforementioned intelligent control system of the cell activator can be implemented as: a business instance, a virtual machine, or hardware devices. For example, the intelligent control system of the cell activator can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, this live streaming service system can be understood as software deployed on a cloud node, used to provide intelligent control services for the cell activator to various user terminals. Alternatively, the intelligent control system of the cell activator can also be implemented as a virtual machine deployed on one or more devices in a cloud node. This virtual machine contains application software for managing various user terminals. Or, the intelligent control system of the cell activator can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices configured to provide intelligent control services for the cell activator to various user terminals.

[0031] In terms of implementation, the intelligent control system and user terminal of the cell activator are mutually compatible. That is, if the intelligent control system of the cell activator is implemented as an application installed on a cloud service platform, then the user terminal is implemented as a client that establishes a communication connection with the application; or if the intelligent control system of the cell activator is implemented as a website, then the user terminal is implemented as a webpage; or if the intelligent control system of the cell activator is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0032] Reference Figure 1 The diagram shown is a functional block diagram of the intelligent control system of a cell activation instrument provided in an embodiment of the present invention.

[0033] The intelligent control system 100 of the cell activator described in this invention can be located on a cloud server. In terms of implementation, it can function as one or more service devices, or as an application installed on the cloud (e.g., a server for intelligent control of the cell activator, a server cluster, etc.), or it can be developed as a website. Depending on the functions implemented, the intelligent control system 100 of the cell activator includes a data acquisition and analysis module 101, a harmony coefficient calculation module 102, an abnormal molecule identification module 103, an abnormal molecule correction module 104, and a health status definition module 105.

[0034] In this embodiment of the invention, in the tracking of intelligent control based on the cell activator, each of the above modules can be implemented independently and called upon other modules. This calling can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the intelligent control system of the cell activator provided by this embodiment of the invention, without modifying the program code, the applicable scope of the intelligent control architecture of the cell activator can be adjusted by adding modules and directly calling them, achieving cluster-based horizontal expansion to quickly and flexibly expand the intelligent control system of the cell activator. In practical applications, the above modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.

[0035] The following describes the components and specific workflow of the intelligent control system of the cell activation instrument, using specific embodiments as examples.

[0036] The data acquisition and analysis module 101 is used to construct the cell activation instrument. Based on the data acquisition module, it acquires the weak magnetic field signal of the target user, performs spectrum analysis on the weak magnetic field signal to obtain cell frequency information, identifies the life macromolecules of the target user, analyzes the cross-linking mode of the life macromolecules, and defines the cross-linking wave equation of the life macromolecules according to the cross-linking mode.

[0037] This invention, through the construction of a data acquisition module for a cell activation instrument, integrates multiple sensors and detection technologies, enabling simultaneous detection of various biological parameters and improving detection efficiency. The data acquisition module refers to the hardware and software system within the cell activation instrument specifically designed for acquiring, converting, and transmitting cell-related signals.

[0038] As an embodiment of the present invention, the data acquisition module for constructing the cell activation instrument includes:

[0039] Construct the data acquisition framework for the cell activation instrument;

[0040] The data acquisition framework is analyzed to determine the types of signals it needs to acquire and the acquisition performance parameters, which include: acquisition rate, resolution, and dynamic range.

[0041] Based on the signal type and the acquisition performance parameters, construct the sensor network of the data acquisition framework;

[0042] The signal processing circuit of the data acquisition framework is defined, wherein the signal processing circuit includes: a signal amplification circuit, a signal filtering circuit, and a signal conditioning circuit;

[0043] Define the analog-to-digital conversion algorithm of the data acquisition framework, and construct the signal processing module of the data acquisition framework based on the analog-to-digital conversion algorithm;

[0044] The signal processing module, the signal processing circuit, and the sensor network are integrated into the data acquisition framework to obtain the data acquisition module of the cell activation instrument.

[0045] The data acquisition framework refers to the architecture used in the cell activator to support data acquisition, processing, and transmission. The signal type refers to the specific type of biological or physical signal that the cell activator needs to detect and process during data acquisition. The acquisition performance parameters are a series of indicators that measure the performance of the data acquisition system; these parameters determine whether the system can effectively capture, convert, and transmit signals. The acquisition rate refers to the number of times the data acquisition system samples the signal per unit time. The resolution refers to the smallest signal change that the system can distinguish and differentiate; it is an important indicator of the accuracy of the sensor and the entire data acquisition system. The dynamic range refers to the range of signal strengths that the system can effectively detect and process, from the smallest detectable signal to the largest unsaturated signal. The sensor network refers to a system composed of multiple sensor nodes that are interconnected via wired or wireless means, working together to monitor, collect, and process information in the environment and transmit data to the user or central processing unit. The signal processing circuit refers to the electronic circuit used to receive, amplify, filter, convert, and transmit the signals output by the sensors. The signal amplification circuit refers to the electronic circuit specifically used to enhance the voltage or current level of the sensor output signal. The signal filtering circuit refers to an electronic circuit used to remove or reduce unwanted frequency components from the signal acquired by the sensor, thereby improving signal quality. The signal conditioning circuit refers to an electronic circuit used to amplify, filter, convert, or otherwise process the raw signal output by the sensor to facilitate further processing by subsequent circuits or systems. The analog-to-digital conversion algorithm refers to the mathematical process and method of converting analog signals into digital signals. The signal processing module refers to a component used to further process and optimize the digital signal obtained after analog-to-digital conversion.

[0046] Optionally, the types of signals and acquisition performance parameters to be acquired by the data acquisition framework can be analyzed using photonics and quantum technology.

[0047] Optionally, the signal processing circuit for determining the data acquisition framework can be determined through multiphysics simulation.

[0048] This invention, through its data acquisition module, enables real-time signal processing and analysis of weak magnetic field signals from a target user, allowing for non-invasive monitoring of physiological parameters and reducing the risk of adverse effects on the user. These weak magnetic field signals refer to extremely minute changes in the magnetic field generated by a living organism (such as the human body); these signals are typically very weak, far below the strength of the Earth's magnetic field.

[0049] This invention, through spectral analysis of the weak magnetic field signal, obtains cell frequency information that can identify specific frequency components related to cell activity, thereby aiding in the identification of certain risks. The cell frequency information refers to the electromagnetic signal frequency characteristics related to cell physiological functions extracted from cell activity using data acquisition and processing techniques.

[0050] As an embodiment of the present invention, the step of performing spectral analysis on the weak magnetic field signal to obtain cell frequency information includes:

[0051] The weak magnetic field signal is preprocessed to obtain a preprocessed magnetic field signal;

[0052] The preprocessed magnetic field signal is converted from analog to digital into a magnetic field digital signal.

[0053] Applying a Fast Fourier Transform to the digital magnetic field signal yields a frequency domain signal;

[0054] Perform spectral analysis on the frequency domain signal to obtain the signal spectrum and identify the characteristic peaks of the signal spectrum;

[0055] Based on the characteristic peaks, cell frequency information is extracted from the signal spectrum.

[0056] The preprocessed magnetic field signal refers to the magnetic field signal after a series of processing steps. The digital magnetic field signal refers to the digital form converted from the analog magnetic field signal by the analog-to-digital converter. The frequency domain signal refers to the signal converted from the time domain to the frequency domain after Fast Fourier Transform processing. The signal spectrum refers to the representation of the intensity distribution of a signal at different frequencies. The characteristic peak refers to a significant and meaningful peak in the signal spectrum.

[0057] This invention, through its embodiments, identifies the target user's biomolecules and selects the most suitable biowave frequency and intensity to enhance the therapeutic effect. The biomolecules refer to large molecular compounds that play a crucial role in the body, such as proteins, nucleic acids, and lipids.

[0058] Optionally, as an embodiment of the present invention, the identification of the target user's biomolecules can be achieved by nuclear magnetic resonance spectroscopy.

[0059] This invention, through analyzing the cross-linking mechanisms of the biomolecules, can understand the dynamic changes in intermolecular interactions, improve the efficiency and specificity of signal transmission, and thus achieve more precise signal modulation in quantum biowave modulation. The cross-linking mechanism refers to the method by which stable connections are formed between biomolecules.

[0060] Optionally, as an embodiment of the present invention, the cross-linking mode of the biomolecules can be analyzed by cross-correlation mass spectrometry.

[0061] This invention, by defining a cross-linking wave equation for biomolecules based on the cross-linking method, can better understand how quantum effects influence the cross-linking of biomolecules, thus providing a theoretical basis for quantum biowave modulation. The cross-linking wave equation is a mathematical model used to describe the dynamic behavior and wave characteristics of biomolecules (such as proteins, nucleic acids, polysaccharides, etc.) during the cross-linking process.

[0062] As an embodiment of the present invention, defining the crosslinking wave equation of the biomolecule according to the crosslinking mode includes:

[0063] Based on the described crosslinking method, determine the crosslinking reaction type and crosslinking reaction mechanism of the biomolecules;

[0064] Based on the cross-linking reaction type and the cross-linking reaction mechanism, the reaction rate equation of the biomolecule is constructed.

[0065] Calculate the forward and reverse reaction rates of the biomolecules based on the aforementioned reaction rate equation.

[0066] Analyze the diffusion behavior of the biomolecules, and calculate the diffusion coefficient of the biomolecules based on the diffusion behavior;

[0067] The quantum state of the biomolecule is determined, and the wave function of the biomolecule is constructed based on the quantum state.

[0068] Based on the forward reaction rate, the reverse reaction rate, the diffusion coefficient, and the wave function, a crosslinking wave equation for the biomolecule is defined, wherein the crosslinking wave equation includes:

[0069]

[0070] Where x represents the imaginary unit, satisfying x 2 =-1, h represents a constant, π represents pi, b(w,t) represents the wave function, w represents the position of the biomolecule, and t represents time. Let D represent the rate of change of the wave function with time t, S represent the kinetic energy operator, A represent uncrosslinked type A biomolecules, B represent uncrosslinked type B biomolecules, AB represent the crosslinked molecule of molecules A and B, and K represent the crosslinked molecule of molecules A and B. i This represents the diffusion coefficient of the i-th type of biomolecule. Let c represent the Laplace operator. i (w,t) represents the change in concentration of the i-th type of biomolecule with position w and time t. q represents the rate of change of the concentration of the i-th type of biomolecule over time. i f represents the forward reaction rate of the i-th type of biomolecule. i c represents the reverse reaction rate of the i-th type of biomolecule. A (w,t) represents the concentration of type A biomolecules at position w and time t, c B (w,t) represents the concentration of type B biomolecules at position w and time t, c AB (w,t) represents the concentration of the cross-linked product AB at position w and time t.

[0071] The cross-linking reaction types refer to different ways in which stable connections are formed between biomolecules. These methods can be classified according to the chemical properties and mechanisms of cross-linking. The cross-linking reaction mechanism refers to the process and principle of cross-linking between biomolecules. The reaction rate equation is an equation describing the relationship between the chemical reaction rate and the reactant concentration. The forward reaction rate refers to the rate at which reactants are converted into products in a reversible reaction. The reverse reaction rate refers to the rate at which products are converted into reactants in a reversible reaction. The diffusion behavior refers to the process by which material particles spontaneously move from a high-concentration region to a low-concentration region due to thermal motion. The diffusion coefficient is a physical quantity describing the diffusion rate of a substance in a medium. The quantum state refers to the state of a quantum system at a specific moment. The wave function is a mathematical function used to describe the state of a quantum system. The imaginary unit is a special symbol used in mathematics to represent imaginary numbers. The kinetic energy operator is an operator used to describe the kinetic energy of a particle. The potential energy operator is an operator used to describe the potential energy of a particle in a potential field. The Laplace operator is a multivariable differential operator.

[0072] Optionally, the reaction rate equation for the biomolecule based on the crosslinking reaction type and the crosslinking reaction mechanism can be constructed using a kinetic model.

[0073] The harmonic coefficient calculation module 102 is used to fit the target user's biological wave digital model based on the cell frequency information and the cross-linking wave equation, analyze the origin life state and dynamic life state of the biomolecules based on the biological wave digital model, calculate the origin frequency and dynamic frequency of the origin life state and the dynamic life state, and analyze the harmonic coefficient of the origin frequency and the dynamic frequency.

[0074] This invention, through fitting a biological wave digital model of the target user based on the cell frequency information and the cross-linking wave equation, can accurately simulate the wave characteristics of an individual organism, thereby better analyzing the physiological state of the organism. The biological wave digital model refers to a theoretical mathematical model designed to simulate and describe the wave characteristics of biomolecules within a living organism.

[0075] Optionally, as an embodiment of the present invention, the fitting of the target user's bio-wave digital model based on the cell frequency information and the cross-linking wave equation can be performed using a genetic algorithm.

[0076] This invention, through the analysis of the origin and dynamic life states of biomolecules based on the aforementioned biological fluctuation digital model, can identify abnormal changes in these states, thereby gaining a deeper understanding of the dynamic behavior of biomolecules under different physiological states. The origin life state refers to the structural and functional characteristics of biomolecules in their natural, undisturbed state. The dynamic life state refers to the state of structural and functional changes experienced by biomolecules during their life cycle.

[0077] As an embodiment of the present invention, the analysis of the origin and dynamic states of the biomolecules based on the biological wave digital model includes:

[0078] Define the molecular structure and functional characteristics of the biomolecules in an undisturbed state;

[0079] Based on the molecular structure and functional characteristics, the baseline parameters of the biomolecules are determined using the biological wave digital model.

[0080] Based on the aforementioned benchmark parameters, the original life state of the biomolecules is determined;

[0081] Constructing a responsive stimulus environment for the aforementioned biomacromolecules;

[0082] Based on the aforementioned response stimulus environment, the dynamic behavior of the biomolecules is analyzed using the aforementioned biological wave digital model;

[0083] Based on the dynamic behavior, the dynamic life state of the biomolecules is identified.

[0084] The molecular structure refers to the spatial arrangement and three-dimensional morphology of biological macromolecules. The functional characteristics refer to the ability and properties of biological macromolecules to perform specific biological functions within an organism. The benchmark parameters refer to key parameters used as reference or comparison standards in specific analyses. The responsive stimulus environment refers to external or internal conditions that can elicit specific responses from biological systems (such as cells, tissues, or organs). The dynamic behavior refers to the behavioral patterns or activities exhibited by organisms, systems, organs, cells, or molecules over time.

[0085] Optionally, the responsive stimulus environment for constructing the biomolecule can be constructed using microfluidic technology.

[0086] This invention, through calculation of the origin frequency and dynamic frequency of the aforementioned origin life state and dynamic life state, can identify abnormally related biomolecular frequency changes by comparing the dynamic frequency under abnormal conditions with the origin frequency under normal conditions, thereby enabling early regulation of abnormal states. The origin frequency refers to the inherent vibrational frequency of a biological system or biomolecule under baseline conditions. The dynamic frequency refers to the vibrational frequency of a biomolecule during a specific dynamic process.

[0087] Optionally, as an embodiment of the present invention, the calculation of the origin frequency and the moving frequency of the origin life state and the moving life state can be performed by spectral analysis technology.

[0088] This invention, through analyzing the harmonic coefficients of the origin frequency and the moving frequency, can determine the response of a biological system to external stimuli or internal changes, thereby identifying abnormal changes in the biological system. The harmonic coefficient is a quantitative indicator used to describe the relationship between two frequencies.

[0089] As an embodiment of the present invention, the analysis of the harmonic coefficients of the origin frequency and the moving point frequency includes:

[0090] Calculate the origin frequency and the moving point frequency's origin spectrum and moving point spectrum, respectively;

[0091] The original self-spectral density and the moving self-spectral density of the original spectrum and the moving spectrum are analyzed respectively.

[0092] By intersecting the original spectrum and the moving point spectrum, a cross-spectral density function is obtained.

[0093] Extract the cross-spectral density amplitude of the cross-spectral density function;

[0094] Based on the cross-spectral density amplitude, the origin self-spectral density, and the moving point self-spectral density, the harmonic coefficients of the origin frequency and the moving point frequency are calculated using the following formula:

[0095]

[0096] Where θ represents the harmony coefficient, F h (p) represents the cross-spectral density amplitude, M0(p) represents the origin auto-spectral density, M d (p) represents the dynamic point autospectral density.

[0097] The origin spectrum refers to the frequency distribution obtained by performing a Fourier transform on the signal at the origin. The moving-point spectrum refers to the frequency distribution obtained by performing a Fourier transform on the signal at a moving point. The origin autospectral density refers to the Fourier transform of a random signal when its autocorrelation function has zero lag time. The moving-point autospectral density refers to the time-varying spectral density function obtained by performing a Fourier transform on the autocorrelation function of a signal. The cross-spectral density function is a concept in signal processing that describes the correlation between two different time series in the frequency domain. The cross-spectral density magnitude refers to the absolute value or modulus of the cross-spectral density function, quantifying the strength of the mutual correlation between two time series at a specific frequency.

[0098] The abnormal molecule identification module 103 is used to define the physiological state of the target user as a sub-healthy state when the harmonic coefficient does not meet the preset harmonic coefficient threshold, and to identify the abnormal biomolecules of the target user according to the origin frequency and the moving frequency.

[0099] This invention, by defining the target user's physiological state as sub-health when the harmony coefficient does not meet a preset harmony coefficient threshold, can identify potential sub-health states before obvious abnormalities appear, thereby providing quantum spiral bio-wave conditioning. The sub-health state refers to a physiological state typically characterized by mild dysfunction or decline in bodily functions.

[0100] This invention, by identifying abnormal biomolecules in the target user based on the origin frequency and the moving frequency, can more accurately identify abnormalities in biomolecules, thereby providing more accurate biomarkers for biowave therapy. The abnormal biomolecules refer to biomolecules that exhibit specific structural abnormalities and functional disorders under specific conditions within the human body.

[0101] Optionally, as an embodiment of the present invention, the identification of abnormal biomolecules of the target user based on the origin frequency and the moving frequency can be achieved by threshold analysis.

[0102] The abnormal molecule correction module 104 is used to determine the magnetic field strength and frequency of the cell activator based on the abnormal biomolecules and the sub-health state; based on the magnetic field strength and frequency, convert the corresponding magnetic field of the cell activator into terahertz magnetic energy; use the terahertz magnetic energy to induce cell resonance in the target user; based on the cell resonance, stimulate the cell activity of the target cell corresponding to the target user; based on the cell activity, analyze the biological information released by the target cell; and correct the abnormal biomolecules according to the biological information to obtain normal biomolecules.

[0103] This invention, based on the abnormal biomolecules and the sub-healthy state, determines that the magnetic field strength and frequency of the cell activator can enhance the cell's self-repair ability and accelerate the recovery of damaged cells. Here, the magnetic field strength refers to the magnitude of the magnetic field generated by the cell activator. The magnetic field frequency refers to the number of times the magnetic field generated by the cell activator repeats its changes per unit time.

[0104] As an embodiment of the present invention, determining the magnetic field strength and magnetic field frequency of the cell activator based on the abnormal biomolecules and the sub-health state includes:

[0105] Analyze the abnormal manifestations of the abnormal biomolecules and the abnormal state of the sub-health condition.

[0106] Based on the abnormal manifestations and abnormal states, the conditioning targets of the cell activation instrument are determined;

[0107] Based on the conditioning goals, the magnetic field strength range and magnetic field frequency range of the cell activator are preliminarily determined;

[0108] Based on the magnetic field strength range and the magnetic field frequency range, a magnetic field analysis model for the cell activation instrument is constructed.

[0109] Based on the magnetic field analysis model, the magnetic field strength and magnetic field frequency of the conditioning target are mapped.

[0110] The abnormal manifestations refer to abnormal changes in the structure or function of biomolecules at the molecular level. The abnormal state refers to a series of declines or discomforts in physiological, psychological, and social functions. The conditioning goal refers to the specific health improvement or restoration achieved through intervention. The magnetic field strength range refers to the range of magnetic field strength used when using the cell activation instrument. The magnetic field frequency range refers to the frequency range of the magnetic field used when using the cell activation instrument. The magnetic field analysis model refers to a mathematical model used to analyze and predict the behavior of a magnetic field under specific environmental and conditions.

[0111] Optionally, the magnetic field analysis model of the cell activator, constructed based on the magnetic field strength range and the magnetic field frequency range, can be constructed using a multiphysics coupling simulation method.

[0112] This invention, through the conversion of the magnetic field corresponding to the cell activator into terahertz magnetic energy based on the magnetic field strength and frequency, can accelerate wound healing and reduce inflammation by improving blood circulation and promoting cell metabolism. The terahertz magnetic energy refers to electromagnetic energy in the terahertz band.

[0113] As an embodiment of the present invention, the step of converting the magnetic field corresponding to the cell activator into terahertz magnetic energy based on the magnetic field strength and the magnetic field frequency includes:

[0114] Configure the terahertz wave device of the cell activator and determine the frequency range of the terahertz wave corresponding to the terahertz wave device;

[0115] Construct the magnetic field control module for the terahertz wave device;

[0116] Based on the magnetic field strength and the magnetic field frequency, the control parameters of the magnetic field control module are determined;

[0117] Based on the control parameters and the frequency range, the magnetic field and the terahertz wave are effectively coupled to obtain terahertz magnetic energy.

[0118] The terahertz wave device refers to a device capable of generating, emitting, and / or controlling terahertz electromagnetic radiation. The frequency range refers to the operating frequency range of the terahertz wave device, that is, the frequency interval within which the device can generate, emit, or detect terahertz electromagnetic waves. The magnetic field control module refers to the part used to control and manage the magnetic field characteristics of the cell activator. The control parameters refer to the variables used to control and adjust the magnetic field to achieve specific conditioning.

[0119] Optionally, the effective coupling of the magnetic field and the terahertz wave according to the control parameters and the frequency range to obtain terahertz magnetic energy can be achieved through plasma coupling.

[0120] This invention utilizes terahertz magnetic energy to induce cellular resonance in the target user, thereby stimulating cell proliferation and differentiation, increasing intracellular molecular movement, and promoting metabolism. Specifically, cellular resonance refers to the phenomenon where energy from externally applied terahertz waves or other electromagnetic waves causes molecules or structures within the cell to vibrate at frequencies matching the applied frequency.

[0121] This invention, through cell resonance, stimulates the cellular activity of the target cells corresponding to the target user, thereby increasing cellular metabolic rate and enhancing cellular absorption of nutrients and excretion of waste. Cellular activity refers to the ability of cells to perform their biological functions, including growth, division, metabolism, response to external stimuli, and specific biochemical reactions.

[0122] This invention, through analyzing the biological information released by the target cells based on cell activity, can enhance signal transmission between quantum biowaves and cells, thereby improving the therapeutic effect. The biological information refers to information generated, transmitted, and used to regulate biological processes by various molecules, cells, tissues, and organs within the body during life activities.

[0123] Optionally, as an embodiment of the present invention, the analysis of the biological information released by the target cell based on the cell activity can be obtained by molecular biology techniques.

[0124] This invention, through the application of biological information, corrects abnormal biomolecules to obtain normal biomolecules. These normal biomolecules can mitigate the risks caused by the abnormalities of these molecules, thereby restoring normal cellular function. The normal biomolecules refer to molecules that, after conditioning and repair, exist in a normal functional state within the organism.

[0125] The health status definition module 105 is used to calculate the normal origin frequency and normal dynamic frequency of the normal life macromolecules, analyze the normal harmonic coefficient of the normal origin frequency and the normal dynamic frequency, and define the physiological state corresponding to the target user as a healthy state when the normal harmonic coefficient meets the harmonic coefficient threshold.

[0126] This invention analyzes the parameters of a biological macromolecule after conditioning by calculating its normal origin frequency and normal dynamic frequency, thereby analyzing the conditioning effect. The normal origin frequency refers to the inherent vibrational frequency of a biological macromolecule in its natural, undisturbed state, as understood in molecular biology and spectroscopy. The normal dynamic frequency refers to the vibrational frequency of a biological macromolecule at a specific point other than its equilibrium position (origin) under its normal physiological state.

[0127] As an embodiment of the present invention, the calculation of the normal origin frequency and normal dynamic frequency of the normal biomolecule includes:

[0128] The stable conformation of the normal biomolecule is analyzed, and the normal biomolecule is geometrically optimized based on the stable conformation to obtain a molecular equilibrium structure.

[0129] Based on the molecular equilibrium structure, frequency analysis was performed on the normal biomolecules to obtain the normal origin frequency.

[0130] Molecular dynamics simulations were performed on the aforementioned normal biomolecules to obtain their dynamic molecular conformations;

[0131] Based on the aforementioned molecular dynamic conformation, the normal biomolecules are measured using a preset time-resolved spectroscopy technique to obtain spectral data;

[0132] Based on the spectral data, the normal dynamic point frequency of the normal biomolecule is calculated.

[0133] The stable conformation refers to the lowest-energy, most stable three-dimensional structure reached by a biomolecule under specific conditions. The molecular equilibrium structure refers to a state of lowest energy and most stable state reached by a molecule after a certain period of adjustment and interaction under specific environmental conditions. The molecular dynamic conformation refers to the instantaneous morphology of a molecule's three-dimensional structure over a period of time. The preset time-resolved spectroscopy technique is an experimental technique capable of measuring the spectral characteristics of a substance at different time points. The spectral data refers to the set of information about the optical properties of a substance obtained through spectroscopic methods.

[0134] Optionally, the frequency analysis of the normal biomolecules based on the molecular equilibrium structure, and the obtaining of the normal origin frequency, can be obtained through quantum chemical calculations.

[0135] This invention provides data support for subsequent analysis of whether a target user has reached a healthy state by analyzing the normal harmonic coefficients of the normal origin frequency and the normal dynamic frequency. The normal harmonic coefficients refer to the proportional relationships between molecular vibrational frequencies.

[0136] This invention, by defining the physiological state of the target user as a healthy state when the normal harmony coefficient meets the harmony coefficient threshold, can analyze the health status of the target user after treatment, thereby determining the treatment effect of the cell activation device.

[0137] This invention, through its data acquisition module, enables real-time signal processing and analysis of weak magnetic field signals from the target user, allowing for non-invasive monitoring of physiological parameters and reducing the risk of unsuitability for the target user. Optionally, by defining the cross-linking wave equation of the biomolecules based on the cross-linking method, this invention can better understand how quantum effects influence the cross-linking of biomolecules, thus providing a theoretical basis for quantum biowave modulation. Furthermore, by analyzing the harmonic coefficients of the origin frequency and the moving frequency, this invention can determine the response of a biological system to external stimuli or internal changes, thereby identifying abnormal changes in the biological system. This invention, through defining the target user's physiological state as sub-healthy when the harmony coefficient does not meet a preset harmony coefficient threshold, can identify potential sub-healthy states before obvious abnormalities appear, thus providing quantum spiral bio-wave conditioning. Furthermore, by analyzing the biological information released by the target cells based on cell activity, this invention can enhance signal transmission between quantum bio-waves and cells, improving the conditioning effect. Finally, by calculating the normal origin frequency and normal dynamic frequency of normal biomolecules, this invention can analyze the parameters of biomolecules after conditioning, thereby analyzing the conditioning effect. Therefore, this invention can improve the precise control of cell enhancement effects in cell activation instruments.

[0138] like Figure 2 The diagram shown is a flowchart illustrating an intelligent control method for a cell activator according to an embodiment of the present invention. In this embodiment, the intelligent control method for the cell activator includes:

[0139] A data acquisition module for a cell activation instrument is constructed. Based on the data acquisition module, weak magnetic field signals of the target user are acquired. The weak magnetic field signals are subjected to spectrum analysis to obtain cell frequency information. The biomolecules of the target user are identified, and the cross-linking mode of the biomolecules is analyzed. Based on the cross-linking mode, the cross-linking wave equation of the biomolecules is defined.

[0140] Based on the cell frequency information and the cross-linking wave equation, a biological wave digital model of the target user is fitted. Based on the biological wave digital model, the origin life state and dynamic life state of the biomolecule are analyzed, the origin frequency and dynamic frequency of the origin life state and the dynamic life state are calculated, and the harmony coefficient of the origin frequency and the dynamic frequency is analyzed.

[0141] When the harmonic coefficient does not meet the preset harmonic coefficient threshold, the physiological state corresponding to the target user is defined as a sub-healthy state, and the abnormal biomolecules of the target user are identified based on the origin frequency and the moving frequency.

[0142] Based on the abnormal biomolecules and the sub-healthy state, the magnetic field strength and frequency of the cell activator are determined. Based on the magnetic field strength and frequency, the corresponding magnetic field of the cell activator is converted into terahertz magnetic energy. The terahertz magnetic energy is used to induce cell resonance in the target user. Based on the cell resonance, the cell activity of the target cell corresponding to the target user is stimulated. Based on the cell activity, the biological information released by the target cell is analyzed. Based on the biological information, the abnormal biomolecules are corrected to obtain normal biomolecules.

[0143] Calculate the normal origin frequency and normal dynamic frequency of the normal biomolecules, analyze the normal harmonic coefficients of the normal origin frequency and the normal dynamic frequency, and define the physiological state of the target user as a healthy state when the normal harmonic coefficients meet the harmonic coefficient threshold.

[0144] In the several embodiments provided by this invention, it should be understood that the provided systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0145] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent control system for a cell activation instrument, characterized in that, The intelligent control of the cell activator includes: a data acquisition and analysis module, a harmony coefficient calculation module, an abnormal molecule identification module, an abnormal molecule correction module, and a health status definition module; The data acquisition and analysis module is used to construct the data acquisition module of the cell activation instrument. Based on the data acquisition module, it acquires the weak magnetic field signal of the target user, performs spectral analysis on the weak magnetic field signal to obtain cell frequency information, identifies the biomolecules of the target user, analyzes the cross-linking mode of the biomolecules, and defines the cross-linking wave equation of the biomolecules based on the cross-linking mode. The cross-linking wave equation includes: determining the cross-linking reaction type and cross-linking reaction mechanism of the biomolecules based on the cross-linking mode; constructing the reaction rate equation of the biomolecules based on the cross-linking reaction type and the cross-linking reaction mechanism; calculating the forward reaction rate and reverse reaction rate of the biomolecules based on the reaction rate equation; analyzing the diffusion behavior of the biomolecules; calculating the diffusion coefficient of the biomolecules based on the diffusion behavior; determining the quantum state of the biomolecules; constructing the wave function of the biomolecules based on the quantum state; and defining the cross-linking wave equation of the biomolecules based on the forward reaction rate, the reverse reaction rate, the diffusion coefficient, and the wave function. The cross-linking wave equation includes: in, Represents the imaginary unit, satisfying 2 =-1, Represents a constant. Represents pi (π). Represents the wave function. Indicates the location of biomolecules. Indicates time, Represents the wave function as a function of time rate of change, Represents the kinetic energy operator. Represents the potential energy operator. Indicates uncrosslinked biomolecules Indicates uncrosslinked biomolecules Indicates molecule and molecules Cross-linked molecules, Indicates the first The diffusion coefficient of biomolecular molecules Represents the Laplace operator. Indicates the first The concentration of biomolecules varies with location and time Changes Indicates the first The rate of change of concentration of biomolecular macromolecules over time. Indicates the first Forward reaction rate of biomolecular macromolecules Indicates the first The reverse reaction rate of biomolecular molecules express biomolecular positions and time concentration, express biomolecular positions and time concentration, Indicates cross-linked products In position and time The concentration; The harmonic coefficient calculation module is used to fit the target user's biological wave digital model based on the cell frequency information and the cross-linking wave equation, analyze the origin life state and dynamic life state of the biomolecules based on the biological wave digital model, calculate the origin frequency and dynamic frequency of the origin life state and the dynamic life state, and analyze the harmonic coefficient of the origin frequency and the dynamic frequency. The health status definition module is used to calculate the normal origin frequency and normal dynamic frequency of normal life macromolecules, analyze the normal harmonic coefficient of the normal origin frequency and the normal dynamic frequency, and define the physiological state of the target user as a healthy state when the normal harmonic coefficient meets the harmonic coefficient threshold.

2. The intelligent control system of the cell activation instrument as described in claim 1, characterized in that, The abnormal molecule identification module is used to define the physiological state of the target user as a sub-healthy state when the harmonic coefficient does not meet the preset harmonic coefficient threshold, and to identify the abnormal biomolecules of the target user based on the origin frequency and the movement frequency. The abnormal molecule correction module is used to determine the magnetic field strength and frequency of the cell activator based on the abnormal biomolecules and the sub-health state. Based on the magnetic field strength and frequency, the corresponding magnetic field of the cell activator is converted into terahertz magnetic energy. The terahertz magnetic energy is used to induce cell resonance in the target user. Based on the cell resonance, the cell activity of the target cell corresponding to the target user is stimulated. Based on the cell activity, the biological information released by the target cell is analyzed. Based on the biological information, the abnormal biomolecules are corrected to obtain normal biomolecules.

3. The intelligent control system of the cell activation instrument as described in claim 1, characterized in that, The data acquisition module for constructing the cell activation instrument includes: Construct the data acquisition framework for the cell activation instrument; The data acquisition framework is analyzed to determine the types of signals it needs to acquire and the acquisition performance parameters, which include: acquisition rate, resolution, and dynamic range. Based on the signal type and the acquisition performance parameters, construct the sensor network of the data acquisition framework; The signal processing circuit of the data acquisition framework is defined, wherein the signal processing circuit includes: a signal amplification circuit, a signal filtering circuit, and a signal conditioning circuit; Define the analog-to-digital conversion algorithm of the data acquisition framework, and construct the signal processing module of the data acquisition framework based on the analog-to-digital conversion algorithm; The signal processing module, the signal processing circuit, and the sensor network are integrated into the data acquisition framework to obtain the data acquisition module of the cell activation instrument.

4. The intelligent control system of the cell activation instrument as described in claim 1, characterized in that, The step of performing spectral analysis on the weak magnetic field signal to obtain cell frequency information includes: The weak magnetic field signal is preprocessed to obtain a preprocessed magnetic field signal; The preprocessed magnetic field signal is converted from analog to digital into a magnetic field digital signal. Applying a Fast Fourier Transform to the digital magnetic field signal yields a frequency domain signal; Perform spectral analysis on the frequency domain signal to obtain the signal spectrum and identify the characteristic peaks of the signal spectrum; Based on the characteristic peaks, cell frequency information is extracted from the signal spectrum.

5. The intelligent control system of the cell activation instrument as described in claim 1, characterized in that, The analysis of the origin and dynamic states of the biomolecules based on the aforementioned biological wave digital model includes: Define the molecular structure and functional characteristics of the biomolecules in an undisturbed state; Based on the molecular structure and functional characteristics, the baseline parameters of the biomolecules are determined using the biological wave digital model. Based on the aforementioned benchmark parameters, the original life state of the biomolecules is determined; Constructing a responsive stimulus environment for the aforementioned biomacromolecules; Based on the aforementioned response stimulus environment, the dynamic behavior of the biomolecules is analyzed using the aforementioned biological wave digital model; Based on the dynamic behavior, the dynamic life state of the biomolecules is identified.

6. The intelligent control system of the cell activation instrument as described in claim 1, characterized in that, The analysis of the harmonic coefficients of the origin frequency and the moving frequency includes: Calculate the origin frequency and the moving point frequency's origin spectrum and moving point spectrum, respectively; The original self-spectral density and the moving self-spectral density of the original spectrum and the moving spectrum are analyzed respectively. By intersecting the original spectrum and the moving point spectrum, a cross-spectral density function is obtained. Extract the cross-spectral density amplitude of the cross-spectral density function; The harmonic coefficients of the origin frequency and the moving frequency are calculated based on the cross-spectral density amplitude, the origin self-spectral density, and the moving point self-spectral density.

7. The intelligent control system of the cell activation instrument as described in claim 2, characterized in that, The determination of the magnetic field strength and frequency of the cell activator based on the abnormal biomolecules and the sub-health state includes: Analyze the abnormal manifestations of the abnormal biomolecules and the abnormal state of the sub-health condition. Based on the abnormal manifestations and abnormal states, the conditioning targets of the cell activation instrument are determined; Based on the conditioning goals, the magnetic field strength range and magnetic field frequency range of the cell activator are preliminarily determined; Based on the magnetic field strength range and the magnetic field frequency range, a magnetic field analysis model for the cell activation instrument is constructed. Based on the magnetic field analysis model, the magnetic field strength and magnetic field frequency of the conditioning target are mapped.

8. The intelligent control system of the cell activation instrument as described in claim 7, characterized in that, The process of converting the magnetic field corresponding to the cell activator into terahertz magnetic energy based on the magnetic field strength and the magnetic field frequency includes: Configure the terahertz wave device of the cell activator and determine the frequency range of the terahertz wave corresponding to the terahertz wave device; Construct the magnetic field control module for the terahertz wave device; Based on the magnetic field strength and the magnetic field frequency, the control parameters of the magnetic field control module are determined; Based on the control parameters and the frequency range, the magnetic field and the terahertz wave are effectively coupled to obtain terahertz magnetic energy.

9. The intelligent control system of the cell activation instrument as described in claim 1, characterized in that, The calculation of the normal origin frequency and normal dynamic frequency of the normal biomolecules includes: The stable conformation of the normal biomolecule is analyzed, and the normal biomolecule is geometrically optimized based on the stable conformation to obtain a molecular equilibrium structure. Based on the molecular equilibrium structure, frequency analysis was performed on the normal biomolecules to obtain the normal origin frequency. Molecular dynamics simulations were performed on the aforementioned normal biomolecules to obtain their dynamic molecular conformations; Based on the aforementioned molecular dynamic conformation, the normal biomolecules are measured using a preset time-resolved spectroscopy technique to obtain spectral data; Based on the spectral data, the normal dynamic point frequency of the normal biomolecule is calculated.

Images