Biological information targeting processing method and system based on biological wave resonance dynamic induction

Abstract

The application relates to the technical field of biological wave resonance dynamic induction, and provides a biological information targeting processing method and system based on biological wave resonance dynamic induction, so as to solve the problems of low space-time accuracy and poor dynamic adaptability of biological energy targeting regulation in the prior art. The method comprises the following steps: acquiring dynamic change data of a biological surface temperature field, combining a heat conduction model to calculate an internal heat source diffusion rate, and synchronously correlating modeling to generate resonance energy distribution characteristics; based on the real-time matching relationship between the thermodynamic fluctuation amplitude change rate and the biological wave resonance frequency offset in the characteristics, an adaptive energy output sequence is dynamically generated; directional energy stimulation is implemented on the biological surface, so that the energy density distribution of the stimulation result can track the change of the heat source diffusion rate in real time, the resonance frequency band can automatically match the biological wave inherent frequency band, and closed-loop targeting regulation of dynamic coupling of biological wave phase delay and thermodynamic parameters is realized. The application improves the space-time accuracy and dynamic adaptability of biological energy targeting regulation.

Description

Technical Field

[0001] This application relates to the field of bio-wave resonance dynamic sensing technology, and in particular to a bio-information targeted processing method and system based on bio-wave resonance dynamic sensing. Background Technology

[0002] In the field of bioenergy targeted therapy, it is necessary to sense changes in the temperature field of the biological surface in real time and dynamically adjust the energy output to achieve adaptive matching between the biowave resonant frequency and thermodynamic parameters. Existing technologies struggle to simultaneously satisfy the spatiotemporal precision and dynamic adaptability of energy stimulation, especially in scenarios where the heat source diffusion rate changes rapidly, traditional methods cannot achieve closed-loop control.

[0003] Currently, a static energy feedback system based on infrared thermal imaging is used. This system collects the surface temperature distribution of organisms using a fixed array of sensors, calculates energy output parameters using a preset thermal conductivity matrix, and then irradiates the area with constant-frequency pulse waves. The system adjusts the output intensity through periodic temperature sampling, achieving basic energy regulation under steady-state heat source conditions.

[0004] Static energy feedback systems rely on a preset thermal conductivity coefficient. When the anisotropic conduction characteristics of biological tissue change, the energy output parameters deviate from the actual requirements. Fixed-frequency pulse waves are difficult to cover the dynamic shift of the biological wave resonant frequency band, resulting in a mismatch between energy stimulation and biological wave resonance. Periodic temperature sampling introduces response delay, and regulation lag occurs when the heat source diffusion rate changes abruptly. Summary of the Invention

[0005] This application provides a biological information targeted processing method and system based on bio-wave resonance dynamic sensing, which solves the problems of low spatiotemporal accuracy and poor dynamic adaptability of bioenergy targeted regulation in the prior art.

[0006] In a first aspect, this application provides a bio-information targeted processing method based on bio-wave resonance dynamic sensing, comprising:

[0007] To acquire dynamic change data of the temperature field on the surface of organisms;

[0008] Based on the dynamic change data and combined with the heat conduction model, the heat source diffusion rate inside the newborn object is calculated;

[0009] The dynamic change data is synchronously correlated with the heat source diffusion rate to generate resonant energy distribution characteristics;

[0010] The rate of change of thermodynamic fluctuation amplitude in the resonant energy distribution characteristics is matched with the bio-wave resonant frequency offset in real time to generate an energy output sequence.

[0011] The energy output sequence is used to apply targeted energy stimulation to the surface of the organism to obtain the targeted energy stimulation result. The energy density distribution of the targeted energy stimulation result follows the heat source diffusion rate in real time, and the resonant frequency band of the energy density distribution dynamically covers the inherent frequency band of the biological wave, so as to realize closed-loop targeted regulation of biological wave phase delay and thermodynamic parameters.

[0012] Optionally, the step of real-time matching of the rate of change of thermodynamic fluctuation amplitude in the resonant energy distribution characteristics with the bio-wave resonant frequency offset to generate an energy output sequence includes:

[0013] Establish a dynamic correspondence between the rate of change of thermodynamic fluctuation amplitude and the offset of biological wave resonant frequency in the aforementioned resonance energy distribution characteristics;

[0014] The heat source diffusion rate is converted into a biological wave band offset compensation parameter through the dynamic correspondence.

[0015] The frequency offset compensation parameters of the biological wave band and the rate of change of the thermodynamic fluctuation amplitude are coupled and analyzed to generate an energy output sequence.

[0016] Optionally, the coupled analysis of the bio-wave frequency band offset compensation parameter and the rate of change of thermodynamic fluctuation amplitude to generate an energy output sequence includes:

[0017] The bio-wave frequency band offset compensation parameter is decomposed into a frequency band baseline component and a frequency band offset component, and the thermodynamic fluctuation amplitude change rate is decomposed into a steady-state component and a dynamic fluctuation component.

[0018] The frequency band baseline component and the steady-state component are coupled by energy intensity to form a basic waveform parameter set. At the same time, the frequency band offset component and the dynamic fluctuation component are phase correlated to form a dynamic modulation parameter set.

[0019] Based on the aforementioned basic waveform parameter set, an energy reference frequency and duty cycle range are generated, while based on the aforementioned dynamic modulation parameter set, a frequency offset and duty cycle adjustment gradient are generated.

[0020] Based on the energy reference frequency and the duty cycle range, initial waveform parameters are generated, and the frequency offset and the duty cycle adjustment gradient are superimposed on the initial waveform parameters as corresponding dynamic modulation quantities to generate target waveform parameters.

[0021] Based on the time-varying characteristics of the rate of change of the thermodynamic fluctuation amplitude, the dynamic modulation amount is dynamically applied to the target waveform parameters to generate waveform modulation parameters;

[0022] The energy emission device is driven by the waveform modulation parameters to generate an energy output sequence.

[0023] Optionally, the step of coupling the frequency band baseline component with the steady-state component by energy intensity to form a basic waveform parameter set, and simultaneously performing phase correlation processing on the frequency band offset component and the dynamic fluctuation component to form a dynamic modulation parameter set, includes:

[0024] The baseline component of the frequency band is decomposed into a reference frequency component and a reference amplitude component, and the steady-state component is decomposed into a thermodynamic steady-state intensity component and a thermodynamic steady-state time component;

[0025] The product of the reference amplitude component and the thermodynamic steady-state intensity component is used as the energy intensity reference value. At the same time, the reference frequency component and the thermodynamic steady-state time component are weighted and synthesized to generate waveform time reference parameters.

[0026] The frequency band offset component is decomposed into a frequency shift direction component and a frequency shift amplitude component, and the dynamic fluctuation component is decomposed into a thermodynamic fluctuation direction component and a thermodynamic fluctuation amplitude component.

[0027] A phase correlation index is established by the correspondence between the frequency shift direction component and the thermodynamic wave direction component in the time dimension, and the frequency shift amplitude component and the thermodynamic wave amplitude component are scaled proportionally to generate dynamic modulation amplitude parameters.

[0028] The energy intensity reference value and the waveform time reference parameter are combined to form a basic waveform parameter set, and the phase correlation index and the dynamic modulation amplitude parameter are combined to form a dynamic modulation parameter set.

[0029] Optionally, the step of calculating the heat source diffusion rate inside the newborn object based on the dynamically changing data and in conjunction with a heat conduction model includes:

[0030] Extract periodic fluctuation features corresponding to the temperature field on the surface of the organism from the dynamic change data;

[0031] The periodic fluctuation characteristics are input into the heat conduction model. Based on the anisotropic conduction parameters preset in the heat conduction model, the periodic fluctuation characteristics are decomposed into heat flow vectors in the three-dimensional spatial coordinate system to obtain the heat flow attenuation coefficients in different conduction directions.

[0032] The extreme points of the temperature gradient in the periodic fluctuation characteristics are calculated by backpropagation using the constraint equations corresponding to the heat conduction model, and the heat source diffusion rate is output. The constraint equations are used to characterize the nonlinear relationship between the heat flow attenuation coefficient and the distribution characteristics of the conduction medium.

[0033] Optionally, the step of synchronously correlating and modeling the dynamic change data with the heat source diffusion rate to generate resonant energy distribution characteristics includes:

[0034] Establish a phase delay mapping relationship between the temperature gradient corresponding to the dynamically changing data and the heat source diffusion rate;

[0035] The temperature gradient corresponding to the abrupt change in the heat source diffusion rate is identified by the phase delay mapping relationship.

[0036] Based on the temperature gradient corresponding to the mutation point, the temperature gradient phase-sensitive region where the mutation point is located is determined. The heat conduction path of the temperature gradient phase-sensitive region is spatially superimposed with the propagation path of the biological wave to generate resonant energy distribution characteristics.

[0037] Optionally, the step of applying targeted energy stimulation to the surface of the organism using the energy output sequence to obtain the targeted energy stimulation result includes:

[0038] Based on the waveform duty cycle adjustment factor in the energy output sequence, an energy pulse sequence that matches the spatial distribution of the temperature field on the surface of the organism is generated.

[0039] Based on the frequency band switching time window in the energy output sequence, a phase synchronization marker point corresponding to the boundary frequency of the inherent frequency band of the biological wave is embedded in the energy pulse sequence;

[0040] The phase synchronization markers guide the emission timing of the energy pulse sequence so that the peak energy density of the energy pulse sequence covers the abrupt change in the heat source diffusion rate.

[0041] During energy stimulation, when the spatiotemporal distribution of the heat conduction rate and the peak energy density is detected to deviate from the preset correlation curve, the embedding position of the phase synchronization marker is dynamically adjusted based on the heat conduction rate feedback data of the energy pulse sequence's action area to recalibrate the emission timing of the energy pulse sequence.

[0042] The time-corrected energy pulse sequence is applied to the surface of the organism to generate a targeted energy stimulation result.

[0043] Secondly, this application provides a bio-information targeted processing system based on bio-wave resonance dynamic sensing, comprising:

[0044] The acquisition module is used to acquire dynamic change data of the temperature field on the surface of organisms;

[0045] The calculation module is used to calculate the heat source diffusion rate inside the newborn object based on the dynamically changing data and in conjunction with the heat conduction model.

[0046] The generation module is used to synchronously correlate and model the dynamic change data with the heat source diffusion rate to generate resonant energy distribution characteristics.

[0047] The matching module is used to match the rate of change of thermodynamic fluctuation amplitude in the resonant energy distribution characteristics with the bio-wave resonant frequency offset in real time to generate an energy output sequence.

[0048] The stimulation module is used to perform targeted energy stimulation on the surface of the organism through the energy output sequence to obtain the targeted energy stimulation result. The energy density distribution of the targeted energy stimulation result follows the heat source diffusion rate in real time, and the resonant frequency band of the energy density distribution dynamically covers the inherent frequency band of the biological wave, so as to realize closed-loop targeted regulation of biological wave phase delay and thermodynamic parameters through dynamic coupling.

[0049] Thirdly, this application provides a computing device, including a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a bio-information targeted processing method based on bio-wave resonance dynamic sensing as described in any of the first aspects.

[0050] Fourthly, this application provides a computer storage medium storing computer program instructions thereon, wherein when the computer program instructions are executed by a processor, they implement the bio-information targeted processing method based on bio-wave resonance dynamic sensing as described in any one of the first aspects.

[0051] This application provides a bio-information targeted processing method based on bio-wave resonance dynamic sensing. The method includes: acquiring dynamic change data of the temperature field on the surface of a living organism; calculating the heat source diffusion rate inside the organism based on the dynamic change data and a heat conduction model; synchronously associating the dynamic change data with the heat source diffusion rate to generate resonance energy distribution characteristics; matching the rate of change of thermodynamic fluctuation amplitude in the resonance energy distribution characteristics with the bio-wave resonant frequency offset in real time to generate an energy output sequence; and applying directional energy stimulation to the surface of the organism using the energy output sequence to obtain a directional energy stimulation result. The energy density distribution of the directional energy stimulation result follows the heat source diffusion rate in real time, and the resonant frequency band of the energy density distribution dynamically covers the inherent frequency band of the bio-wave, thereby achieving closed-loop targeted control of the dynamic coupling between the bio-wave phase delay and thermodynamic parameters.

[0052] The technical solution provided in this application has the following beneficial effects:

[0053] This application enables real-time monitoring of surface temperature distribution in organisms, providing high spatiotemporal resolution input data for thermodynamic analysis. Through modeling calculations, it reveals the dynamic characteristics of internal heat sources in organisms and establishes a correlation between surface and internal heat conduction. It integrates thermodynamics and biowave characteristics to form a phase-sensitive targeted energy interaction spectrum. It achieves adaptive coupling between energy output parameters and the organism's dynamic response, resolving frequency band mismatch issues. This allows the energy density distribution to track heat source changes in real time, while the resonant frequency band dynamically covers the biowave frequency band, achieving coordinated regulation of dual parameters.

[0054] Furthermore, this application also establishes a dynamic correspondence between the rate of change of thermodynamic fluctuation amplitude and the offset of biological wave resonant frequency, transforms the heat source diffusion rate into a biological wave frequency band offset compensation parameter, and then couples the rate of change of thermodynamic fluctuation amplitude to generate an energy output sequence, thereby achieving real-time matching between the energy output parameter and the dynamic response of the organism.

[0055] Furthermore, this process overcomes the limitations of static parameter matching by using a thermodynamic-biological wave dual-parameter dynamic coupling mechanism to enable the energy output sequence to simultaneously respond to changes in the heat source diffusion rate and the shift in the biological wave frequency band, thereby achieving precise adaptation between energy stimulation and the real-time state of the organism.

[0056] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 A flowchart illustrating a bio-information targeted processing method based on bio-wave resonance dynamic sensing, provided for an embodiment of this application;

[0059] Figure 2 A schematic diagram of the structure of a bio-information targeted processing system based on bio-wave resonance dynamic sensing, provided for an embodiment of this application;

[0060] Figure 3 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application. Detailed Implementation

[0061] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0062] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0063] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0064] Figure 1 A flowchart of a bio-information targeted processing method based on bio-wave resonance dynamic sensing provided in this application embodiment is shown below. Figure 1 As shown, the method includes:

[0065] Step 101: Obtain dynamic change data of the temperature field on the surface of the organism.

[0066] In step 101, the organism surface temperature field refers to the spatial distribution of temperature on the organism's surface, consisting of multiple temperature measurement points, reflecting the local tissue metabolism and blood flow status. Dynamic change data represents continuously collected time-series temperature information, including temperature values ​​and their trends over time.

[0067] In this embodiment, a distributed temperature sensor array scans the surface of a living organism at a fixed sampling frequency to record the temperature values ​​of each temperature measurement point in real time. The collected raw temperature data is mapped into a temperature field matrix according to spatial coordinates, with the rows and columns of the matrix corresponding to the sensor layout positions and the matrix element values ​​being the temperature of that point. The temperature field matrix is ​​then processed for time-series alignment, and after removing abnormal jump data, a continuous frame dynamic sequence of the temperature field is output.

[0068] For example, taking the treatment of frozen shoulder as an example, a 6×8 temperature measurement point array is arranged on the patient's shoulder, and temperature data is collected at regular intervals to form a temperature field frame sequence containing 48 temperature measurement points. After verification, a continuous and stable dynamic temperature field input is obtained.

[0069] Step 102: Based on the dynamic change data and combined with the heat conduction model, calculate the heat source diffusion rate inside the newborn object.

[0070] In step 102, the heat conduction model describes the mathematical model of heat transfer in biological tissues, including conduction equations and boundary conditions. The connection between the organism's surface and interior is established through heat conduction characteristics. Dynamic changes in the surface temperature field reflect the diffusion rate of internal heat sources, and the two are synchronously modeled using the heat conduction model. The heat source diffusion rate represents the speed at which heat energy from internal heat sources is transferred to the body surface, reflecting the tissue's thermal conductivity.

[0071] In this embodiment, the dynamic sequence of the temperature field is input into the three-dimensional heat conduction differential equation. Based on the preset thermal conductivity of each layer of biological tissue, the equation is solved by the finite difference method. The extreme points of gradient changes in adjacent frames in the temperature field are extracted as the initial conditions for back calculation. The internal heat source location and diffusion rate distribution map are obtained by iterative solution.

[0072] For example, based on the above shoulder temperature field data, thermal conductivity parameters of tissues such as muscle and fat were set, and by solving the heat conduction equation, a peak region of heat source diffusion rate was found near the scapula. The diffusion rate of this region was calculated to be a specific value.

[0073] Step 103: Synchronously correlate the dynamic change data with the heat source diffusion rate to generate resonant energy distribution characteristics.

[0074] In step 103, synchronous correlation modeling represents establishing a spatiotemporal mapping relationship between the temperature gradient and the heat source diffusion rate. Resonant energy distribution characteristics represent an energy interaction spectrum that integrates thermodynamics and biowave properties, marking the targeted stimulation region.

[0075] In this embodiment, the coordinates of the extreme points of the temperature field are superimposed with the peak region of the heat source diffusion rate, and a temperature-heat source coupling distribution map is generated by spatial interpolation. Based on the resonance characteristics of biological tissues, regions where the thermal fluctuation amplitude exceeds the threshold are marked in the coupling distribution map, and a resonance energy distribution grid containing phase-sensitive nodes is output.

[0076] For example, in shoulder treatment, the peak area of ​​heat source diffusion rate is superimposed with the temperature gradient abrupt change area to identify three key target points, and a distribution grid containing the coordinates and energy intensity of these target points is constructed.

[0077] Step 104: Match the rate of change of thermodynamic fluctuation amplitude in the resonant energy distribution characteristics with the bio-wave resonant frequency offset in real time to generate an energy output sequence.

[0078] In step 104, the rate of change of thermodynamic fluctuation amplitude represents the intensity change of temperature fluctuation in the target area per unit time. This intensity change is extracted by differentiating the temperature fluctuation curve of the target area in the resonance energy distribution characteristics, reflecting the dynamic trend of thermodynamic parameters. The bio-wave resonant frequency offset represents the difference between the inherent resonant frequency of the biological tissue and the actual detected frequency. The inherent resonant frequency of the biological tissue is monitored in real time by a bioelectric signal acquisition device and compared with a preset standard resonant frequency to calculate the deviation between the actual and standard frequencies. The energy output sequence refers to a time-varying energy pulse combination generated based on the real-time matching result of the rate of change of thermodynamic fluctuation amplitude and the bio-wave resonant frequency offset. It includes dynamically adjusted waveform intensity, frequency, and timing parameters to accurately adapt to the real-time state of the organism.

[0079] In this embodiment, the temperature fluctuation curve of the target area is monitored and its rate of change is calculated; bioelectric signals are collected simultaneously to analyze the resonant frequency offset; a two-dimensional adjustment parameter table of fluctuation amplitude-frequency offset is established; the duty cycle and fundamental frequency of the pulse wave are dynamically adjusted according to the parameter table to generate a time-varying energy output sequence.

[0080] For example, when the temperature fluctuation rate of the shoulder target point is detected to increase to a specific value and the resonant frequency deviates by a specific amount, the duty cycle of the pulse wave is adjusted from the initial value to a new specific value, and the fundamental frequency is synchronously adjusted to a new specific value.

[0081] Step 105: Directional energy stimulation is applied to the surface of the organism using the energy output sequence to obtain the directional energy stimulation result. The energy density distribution of the directional energy stimulation result follows the heat source diffusion rate in real time, and the resonant frequency band of the energy density distribution dynamically covers the inherent frequency band of the biological wave, so as to achieve closed-loop targeted regulation of biological wave phase delay and thermodynamic parameters through dynamic coupling.

[0082] In step 105, the directional energy stimulation result refers to the comprehensive response generated after the energy output sequence acts on the surface of the organism. It includes two core features: energy density distribution and resonant frequency band coverage. Its energy distribution changes in real time with the heat source diffusion rate, and the frequency band dynamically matches the characteristics of the biowave. The energy density distribution represents the spatial distribution of energy intensity per unit area. The dynamic coverage of the resonant frequency band indicates that the output energy frequency range matches the changes in the biowave in real time. The inherent frequency band of the biowave refers to the inherent energy resonant frequency range of a specific biological tissue under natural conditions, determined by tissue structure and physiological characteristics, and is the benchmark matching target for energy stimulation. The relationship between "biowave phase delay and thermodynamic parameters" and "thermodynamic fluctuation amplitude and biowave resonant frequency": the former constitutes the basis for the iterative calculation of the latter. The phase delay is manifested as a resonant frequency shift in the frequency domain, while the change in thermodynamic parameters is directly reflected as the thermodynamic fluctuation amplitude. The two establish a bidirectional regulatory relationship through a dynamic coupling feedback mechanism. Biowave phase delay refers to the waveform time lag phenomenon caused by factors such as tissue impedance during the propagation of bioelectric signals, which is manifested as the time difference between the stimulation signal and the organism's response. Thermodynamic parameters are a comprehensive set of parameters including temperature, thermal resistance, thermal conductivity, etc., while "thermodynamic fluctuation amplitude" specifically refers to the fluctuation value of temperature or energy change, which is one of the dynamic manifestation dimensions of the former.

[0083] In this embodiment, the energy output sequence is loaded into a multi-band transmitter, and the energy is focused and emitted according to the target coordinates; the temperature feedback and bioelectric response of the stimulation area are monitored in real time, and when the thermal conduction rate and energy density distribution are detected to deviate from the expected correlation curve, the parameter recalibration mechanism is triggered to update the output sequence.

[0084] For example, when an adjusted energy sequence was fired at three target points on the shoulder, monitoring showed an improved match between thermal conduction rate and energy density, and patients reported reduced pain.

[0085] This method dynamically senses changes in the surface temperature field of a biological organism, accurately inverts the diffusion behavior of internal heat sources, and establishes a dual coupling mechanism of thermodynamics and bio-wave parameters to achieve adaptive and precise control of energy output. During treatment, energy parameters follow the changes in the organism's state in real time, avoiding the control lag of traditional methods and solving the energy waste caused by frequency band mismatch, thus improving the reliability and effectiveness of targeted therapy.

[0086] To address the accuracy issue of dynamically matching the bio-wave frequency band offset with thermodynamic fluctuations, in some embodiments, step 104: real-time matching of the rate of change of thermodynamic fluctuation amplitude in the resonant energy distribution characteristics with the bio-wave resonant frequency offset to generate an energy output sequence includes:

[0087] Step 201: Establish the dynamic correspondence between the rate of change of thermodynamic fluctuation amplitude and the offset of biological wave resonant frequency in the resonant energy distribution characteristics.

[0088] In step 201, the dynamic correspondence refers to the quantitative mapping relationship between the rate of change of thermodynamic fluctuation amplitude and the offset of biological wave resonant frequency, which is used to establish real-time control rules between the two.

[0089] In this embodiment, the thermodynamic wave curves of each target region in the resonance energy distribution characteristics are first extracted, and their change amplitude per unit time is calculated. At the same time, the offset between the current biowave resonant frequency and the standard frequency is obtained through the bioelectric signal analysis module. Then, a two-dimensional relational coordinate system is constructed with the rate of change of thermodynamic wave amplitude on the horizontal axis and the frequency offset on the vertical axis. The optimal matching curve is fitted in this coordinate system as a dynamic correspondence.

[0090] Step 202: Convert the heat source diffusion rate into a biological wave band offset compensation parameter through the dynamic correspondence.

[0091] In step 202, the bio-wave frequency band offset compensation parameter refers to the frequency compensation amount that is dynamically adjusted according to the heat source diffusion rate, which is used to correct the bio-wave frequency band offset.

[0092] In this embodiment, the heat source diffusion rate is input into the dynamic correspondence model. By finding the corresponding point on the matching curve, the frequency compensation suggestion value under the current heat source state is output. Then, combined with the frequency band tolerance range of biological tissue, the suggestion value is smoothed to generate a frequency band offset compensation parameter that can be directly used for waveform modulation.

[0093] Step 203: Perform coupled analysis on the biological wave frequency band offset compensation parameter and the thermodynamic fluctuation amplitude change rate to generate an energy output sequence.

[0094] In step 203, coupling analysis refers to the process of coordinating the frequency band compensation parameters with thermodynamic fluctuation characteristics.

[0095] In this embodiment, the frequency band offset compensation parameter is decomposed into two components: fundamental frequency adjustment and bandwidth expansion. At the same time, the rate of change of thermodynamic fluctuation amplitude is converted into an energy intensity adjustment coefficient. The fundamental frequency adjustment and the energy intensity coefficient are multiplied by a waveform synthesis algorithm to generate a basic waveform. Then, the bandwidth expansion is embedded as a modulation factor into the time-varying characteristics of the waveform, and finally, an energy output sequence with adaptive adjustment capability is output.

[0096] Here is a specific example:

[0097] In the treatment embodiment for frozen shoulder, dynamic temperature field data collected from a 6×8 temperature measurement point array on the shoulder was first analyzed using a heat conduction model. This revealed a peak region of heat source diffusion rate near the scapula, the diffusion rate of which was obtained by solving a three-dimensional heat conduction differential equation. Specifically, the calculation involved iteratively solving the equation by substituting the coordinates of the extreme points of the temperature gradient into the boundary conditions of the heat conduction equation. Time-series analysis was performed on the temperature fluctuation data of the three identified key target points. The rate of change of thermodynamic fluctuation amplitude was calculated as the maximum value of the second derivative of the temperature fluctuation curve. Simultaneously, the current bio-wave resonant frequency offset was obtained through bioelectric signal detection, representing the difference between the measured frequency and the standard frequency. Based on a pre-established dynamic correspondence curve, when the target point temperature fluctuation rate increased to 0.15℃ / s and the frequency offset reached 120Hz, interpolation calculations determined that the pulse wave duty cycle needed to be adjusted from the initial 20% to 35%, and the fundamental frequency from 650kHz to 580kHz. The adjustment parameters were implemented using a coupled analysis algorithm. Specifically, the frequency offset compensation parameter was decomposed into a fundamental frequency adjustment of 70kHz and a bandwidth extension of 50kHz, which were then weighted and fused with the rate of change of thermodynamic fluctuation amplitude. The resulting energy output sequence, applied to three target points, showed in real-time monitoring that the correlation coefficient between heat conduction rate and energy density increased from 0.72 to 0.91, and the patient's shoulder range of motion improved while their pain score decreased by two levels.

[0098] In this embodiment, by establishing a bidirectional dynamic mapping between thermodynamic and biowave parameters, intelligent adaptive adjustment of energy output parameters is achieved, enabling the treatment energy to accurately follow changes in tissue thermodynamic state and match biowave characteristics in real time, thereby improving the accuracy and safety of targeted therapy.

[0099] To further improve the matching accuracy between the energy output sequence and the dynamic characteristics of organisms, in some embodiments, step 203: the coupling analysis of the bio-wave frequency band offset compensation parameter and the rate of change of thermodynamic fluctuation amplitude to generate the energy output sequence includes:

[0100] Step 301: Decompose the biological wave frequency band offset compensation parameter into frequency band baseline component and frequency band offset component, and simultaneously decompose the thermodynamic fluctuation amplitude change rate into steady-state component and dynamic fluctuation component.

[0101] In step 301, the frequency band baseline component refers to the reference frequency adjustment amount in the bio-wave frequency band compensation parameters. The frequency band offset component refers to the dynamic fluctuation adjustment amount relative to the reference frequency. The steady-state component refers to the long-term trend component in the change of thermodynamic fluctuation amplitude. The dynamic fluctuation component refers to the short-term fluctuation component.

[0102] In this embodiment, the frequency band baseline component reflecting the overall trend and the frequency band offset component reflecting the instantaneous change are separated from the biological wave frequency band offset compensation parameter by the moving average method; at the same time, wavelet transform is used to decompose the rate of change of thermodynamic fluctuation amplitude into a low-frequency steady-state component and a high-frequency dynamic fluctuation component.

[0103] Step 302: Couple the frequency band baseline component with the steady-state component by energy intensity to form a basic waveform parameter set, and at the same time perform phase correlation processing on the frequency band offset component and the dynamic fluctuation component to form a dynamic modulation parameter set.

[0104] In step 302, energy intensity coupling refers to the process of fusing the frequency band baseline component and the steady-state component according to a weighted ratio. The basic waveform parameter set refers to the set of waveform reference features generated by coupling the frequency band baseline component and the steady-state component, including the fundamental frequency, reference duty cycle, and duration parameters of the energy output, providing a stable output framework for the energy sequence. Phase correlation processing refers to the process of synchronizing the frequency band offset component and the dynamic fluctuation component in the time dimension. The dynamic modulation parameter set refers to the set of adjustment features generated by correlating the frequency band offset component and the dynamic fluctuation component, including frequency offset, duty cycle adjustment gradient, and timing synchronization markers, used to achieve dynamic fine-tuning of the energy output.

[0105] In this embodiment, the frequency band baseline component and the steady-state component are input into a weighted fusion algorithm, and weight coefficients are assigned according to their contribution to the energy output, and a parameter set containing the reference energy intensity and duration is output. At the same time, a time series alignment algorithm is used to keep the change time of the frequency band offset component synchronized with the peak time of the dynamic fluctuation component, forming a parameter set containing dynamic adjustment timing and amplitude.

[0106] Step 303: Based on the basic waveform parameter set, generate the energy reference frequency and duty cycle range, and simultaneously based on the dynamic modulation parameter set, generate the frequency offset and duty cycle adjustment gradient.

[0107] In step 303, the energy reference frequency refers to the fundamental frequency value of the energy output. The duty cycle range refers to the allowable range of the effective energy percentage in the pulse waveform. The frequency offset refers to the adjustment amount relative to the reference frequency. The duty cycle adjustment gradient refers to the unit adjustment step size of the duty cycle change.

[0108] In this embodiment, the ratio of energy intensity to duration is extracted from the basic waveform parameter set as the energy reference frequency, and the upper and lower limits of the duty cycle are determined based on the fluctuation range of the steady-state component. Simultaneously, the timing parameters in the dynamic modulation parameter set are converted into frequency offsets, and the amplitude parameters are converted into duty cycle adjustment gradients. Specifically, the initial value of the energy reference frequency is calculated by multiplying the energy intensity reference value in the basic waveform parameter set with a preset bio-wave frequency band energy conversion coefficient. Simultaneously, the upper and lower thresholds of the duty cycle range are determined based on the periodicity of the waveform time reference parameters and the ratio of the thermodynamic steady-state time component. The dynamically correlated timing parameters in the dynamic modulation parameter set are converted into a time-frequency mapping table, and the real-time frequency offset is output via a table lookup. Simultaneously, the step size of the duty cycle adjustment gradient is calculated based on the intersection coordinates of the dynamic modulation amplitude parameter and the preset thermodynamic-duty cycle response curve.

[0109] Step 304: Based on the energy reference frequency and the duty cycle range, generate initial waveform parameters, and superimpose the frequency offset and the duty cycle adjustment gradient as the corresponding dynamic modulation amount with the initial waveform parameters to generate target waveform parameters.

[0110] In step 304, the initial waveform parameters refer to the reference waveform characteristics without dynamic modulation. The target waveform parameters refer to the complete waveform characteristics after superimposing dynamic modulation.

[0111] In this embodiment, a standard sine wave is generated based on the energy reference frequency as the initial waveform, and the duty cycle range is converted into the initial pulse width. Then, the frequency offset is superimposed on the frequency parameters of the initial waveform in the form of a frequency offset curve, and the pulse width is gradually adjusted according to the duty cycle adjustment gradient to form waveform parameters with dynamic adjustment capability.

[0112] Step 305: Based on the time-varying characteristics of the rate of change of the thermodynamic fluctuation amplitude, dynamically apply the dynamic modulation amount to the target waveform parameters to generate waveform modulation parameters.

[0113] In step 305, the time-varying characteristic of the thermodynamic fluctuation amplitude change rate refers to the law governing the change of temperature fluctuation intensity in the target region over time. It is obtained by real-time monitoring of temperature field data and calculating the difference in fluctuation amplitude between adjacent time points, reflecting the dynamic evolution of the thermodynamic state of biological tissue. Dynamic loading refers to the process of instantly adjusting the target waveform parameters based on the real-time changing thermodynamic fluctuation characteristics. Specifically, while keeping the time reference of the initial waveform parameters unchanged, the frequency offset is incrementally superimposed onto the energy reference frequency, and the switching time points of the rising and falling edges of the waveform are gradually corrected according to the duty cycle adjustment gradient. Waveform modulation parameters refer to the finally determined waveform control command set.

[0114] In this embodiment, a real-time data stream monitoring the rate of change of thermodynamic fluctuation amplitude is used. When a change in fluctuation characteristics is detected, a dynamic modulation update mechanism is immediately triggered to write the latest frequency offset and duty cycle adjustment gradient into the waveform parameter register, ensuring that the output waveform matches the biological state in real time.

[0115] Step 306: Drive the energy emission device through the waveform modulation parameters to generate an energy output sequence.

[0116] In step 306, the energy transmitting device refers to a programmable multi-band radio frequency transmitter, which includes: a frequency synthesis module (covering the 100kHz-10MHz bioresonant frequency band), a pulse width modulation module (resolution ≤1μs), and a power amplification module (output power dynamic range 0.1-5W). For example, when treating frozen shoulder, the waveform modulation parameters drive the transmitter to generate: a reference frequency of 650kHz (matching the characteristic frequency band of shoulder tissue), a dynamic frequency offset of ±50kHz (following the thermodynamic fluctuations of the inflamed area), and a duty cycle dynamically adjusted from 15-25% (corresponding to the local thermal resistance change gradient), forming an energy output sequence with a pulse interval of 200μs and a peak power of 3W.

[0117] In this embodiment, the waveform parameters that have been dynamically loaded are converted into control signals that the transmitting device can recognize, including frequency control words, duty cycle register values ​​and timing trigger commands, and the driving device outputs an energy sequence that perfectly matches the needs of the organism.

[0118] Here is a specific example:

[0119] In a specific embodiment of the treatment for frozen shoulder, when the system detects a temperature fluctuation rate of 0.15℃ / s at three key target points and a bio-wave frequency offset of 120Hz, the 120Hz frequency band offset compensation parameter is first decomposed. The frequency band baseline component is the long-term trend value of 50Hz obtained by moving average of 7 days of historical data, and the frequency band offset component is the instantaneous fluctuation of 70Hz obtained by subtracting the baseline component from the real-time detection value of 120Hz. Simultaneously, the thermodynamic fluctuation amplitude change rate of 0.15℃ / s is decomposed using wavelet transform to obtain a steady-state component of 0.08℃ / s reflecting the long-term treatment effect and a dynamic fluctuation component of 0.07℃ / s reflecting the immediate tissue response. The 50Hz baseline component and the 0.08℃ / s steady-state component are input into the energy coupling algorithm and weighted according to preset weighting coefficients of 0.6 and 0.4. The output basic waveform parameter set includes a reference frequency of 580kHz and a duty cycle range of 25%-35%, where the reference frequency is obtained by subtracting the baseline component of 50Hz from the standard treatment frequency of 650kHz and then multiplying by the thermodynamic steady-state adjustment coefficient of 0.9. Simultaneously, the 70Hz offset component and the 0.07℃ / s dynamic fluctuation component are time-aligned. When a peak value of the dynamic fluctuation component is detected, the generation of a 70Hz frequency offset and a 5% / s duty cycle adjustment gradient is immediately triggered. These two dynamic parameters are obtained by multiplying the real-time fluctuation amplitude by a preset sensitivity coefficient. Based on these parameters, the system first generates initial waveform parameters: a 580kHz fundamental frequency and a 30% initial duty cycle. Then, dynamic modulation is superimposed to form the target waveform parameters. Finally, when real-time monitoring shows that the rate of change of thermodynamic fluctuation amplitude reaches its maximum value, the system outputs an energy sequence containing a 550kHz operating frequency and a 35% duty cycle.

[0120] In this embodiment, a multi-level parameter decomposition and dynamic loading mechanism is used to achieve millisecond-level response matching between the energy output waveform and the biological state, which not only ensures the stability of the treatment benchmark parameters, but also ensures the accuracy of dynamic adjustment, so that the targeted energy stimulation is always in the optimal working state.

[0121] To further improve the accuracy of waveform parameter generation and dynamic response capability, in some embodiments, step 302: coupling the frequency band baseline component with the steady-state component by energy intensity to form a basic waveform parameter set, and simultaneously performing phase correlation processing on the frequency band offset component and the dynamic fluctuation component to form a dynamic modulation parameter set, includes:

[0122] Step 401: Decompose the frequency band baseline component into a reference frequency component and a reference amplitude component, and decompose the steady-state component into a thermodynamic steady-state intensity component and a thermodynamic steady-state time component.

[0123] In step 401, the reference frequency component refers to the portion of the frequency band baseline component that determines the fundamental frequency of energy output. The reference amplitude component refers to the portion that determines the energy intensity. The thermodynamic steady-state intensity component refers to the portion of the steady-state component that reflects the magnitude of energy demand. The thermodynamic steady-state time component refers to the portion that reflects the duration of energy action.

[0124] In this embodiment, Fourier spectrum analysis is performed on the frequency band baseline component to extract the main frequency component as the reference frequency component and the amplitude component as the reference amplitude component; envelope detection technology is used on the steady-state component to separate the intensity component reflecting energy demand and the periodic component reflecting the duration of action.

[0125] Step 402: The product of the reference amplitude component and the thermodynamic steady-state intensity component is used as the energy intensity reference value. At the same time, the reference frequency component and the thermodynamic steady-state time component are weighted and synthesized to generate waveform time reference parameters.

[0126] In step 402, the energy intensity reference value refers to the basic intensity parameter of the energy output. The waveform time reference parameter refers to the characteristic parameter that determines the timing of energy application.

[0127] In this embodiment, the reference amplitude component and the thermodynamic steady-state intensity component are input into a multiplier for product operation, and the basic intensity value reflecting the comprehensive energy demand is output. At the same time, the reference frequency component and the thermodynamic steady-state time component are input into a weighted calculation unit, and a timing parameter including pulse width and interval time is synthesized according to a preset weighting coefficient.

[0128] Step 403: Decompose the frequency band offset component into frequency shift direction component and frequency shift amplitude component, and decompose the dynamic fluctuation component into thermodynamic fluctuation direction component and thermodynamic fluctuation amplitude component.

[0129] In step 403, the frequency shift direction component refers to the directional index of the frequency band offset change trend. The frequency shift amplitude component refers to the specific value of the frequency band offset. The thermodynamic fluctuation direction component refers to the directional index of the dynamic fluctuation change trend. The thermodynamic fluctuation amplitude component refers to the specific value of the dynamic fluctuation.

[0130] In this embodiment, trend analysis is performed on the frequency band offset component, and its first derivative sign is extracted as the direction component, and its absolute value is used as the amplitude component; an extreme value detection method is used for the dynamic fluctuation component to separate the fluctuation change direction mark and the actual fluctuation value.

[0131] Step 404: Establish a phase correlation index by the correspondence between the frequency shift direction component and the thermodynamic wave direction component in the time dimension, and perform proportional scaling on the frequency shift amplitude component and the thermodynamic wave amplitude component to generate dynamic modulation amplitude parameters.

[0132] In step 404, the phase correlation index refers to the time-series correspondence table between the frequency shift direction and the thermodynamic wave direction. The dynamic modulation amplitude parameter refers to the frequency shift amplitude value after proportional adjustment.

[0133] In this embodiment, a time correspondence matrix between the frequency shift direction component and the thermodynamic wave direction component is established, and the time points in which the two are in the same or opposite directions are marked in the matrix; at the same time, the frequency shift amplitude component and the thermodynamic wave amplitude component are input into the proportional regulator, and the adapted modulation amplitude parameter is output according to the preset scaling factor.

[0134] Step 405: Combine the energy intensity reference value and the waveform time reference parameter to form a basic waveform parameter set, and combine the phase correlation index and the dynamic modulation amplitude parameter to form a dynamic modulation parameter set.

[0135] In this embodiment, the energy intensity reference value and waveform time reference parameters are packaged in a preset format to generate a basic waveform parameter set; at the same time, the phase correlation index and dynamic modulation amplitude parameters are combined and encoded to form a parameter set containing dynamic adjustment information.

[0136] Here is a specific example:

[0137] During the treatment of frozen shoulder, after the system obtains the 50Hz baseline component and the 0.08℃ / s steady-state component, spectral analysis is first performed on the 50Hz component to extract 40Hz as the reference frequency component, and the remaining 10Hz as the reference amplitude component. The 40Hz value is determined through analysis of the dominant frequency of historical treatment data. Simultaneously, the 0.08℃ / s steady-state component is processed using signal decomposition technology to obtain a 0.05℃ / s thermodynamic steady-state intensity component and a 0.03℃ / s thermodynamic steady-state time component. This decomposition ratio is set to 5:3 based on the tissue's thermal conductivity characteristics. The 10Hz reference amplitude component is multiplied by the 0.05℃ / s intensity component to obtain an energy intensity reference value of 0.5Hz·℃ / s. This value is then converted to an actual adjustment value of 500Hz by multiplying by a conversion factor of 1000. Finally, the 40Hz reference frequency component and the 0.03℃ / s time component are weighted and synthesized according to a 6:4 weighting ratio to generate waveform time reference parameters containing an effective frequency component of 32Hz. For the 70Hz frequency band offset component, trend analysis extracts +50Hz as the frequency shift direction component, indicating that the frequency needs to be increased, with the remaining 20Hz as the frequency shift amplitude component. The corresponding 0.07℃ / s dynamic fluctuation component is separated into a +0.04℃ / s thermodynamic fluctuation direction component and a 0.03℃ / s thermodynamic fluctuation amplitude component through extreme value detection. When establishing the time correspondence of the direction components, when the thermodynamic fluctuation direction is detected to be in the same direction as the frequency shift direction, it is marked as an enhancement mode in the phase correlation index. The 20Hz frequency shift amplitude component and the 0.03℃ / s thermodynamic fluctuation amplitude component are scaled at a 2:1 ratio to obtain a 30Hz dynamic modulation amplitude parameter, where the scaling factor is set based on clinical validation results. The final base waveform parameter set includes a 580kHz reference frequency and a 30% reference duty cycle, where the reference frequency is obtained by subtracting the 70Hz modulation amount from the standard 650kHz. The dynamic modulation parameter set includes the enhancement mode marker and the 30Hz modulation amplitude.

[0138] In this embodiment, through multi-level parameter decomposition and fine coupling processing, a deep match between the energy output waveform parameters and the biological characteristics is achieved, which not only ensures the stability of the treatment parameters, but also ensures the timeliness and accuracy of dynamic adjustment, so that the targeted energy therapy is always kept in the best working state.

[0139] To further improve the accuracy of heat source diffusion rate calculation, in some embodiments, step 102: calculating the heat source diffusion rate inside the birth object based on the dynamically changing data and in conjunction with a heat conduction model, includes:

[0140] Step 501: Extract the periodic fluctuation features corresponding to the temperature field of the organism's surface from the dynamic change data.

[0141] In step 501, the periodic fluctuation characteristic refers to the regular fluctuation pattern of the temperature field on the surface of an organism changing over time, including the amplitude, period, and phase information of the temperature change.

[0142] In this embodiment of the application, time series analysis is performed on dynamic temperature field data, the main periodic components are extracted by Fourier transform, and then noise interference is removed by filtering, finally obtaining a periodic characteristic curve that reflects the true thermodynamic state of the organism.

[0143] Step 502: Input the periodic fluctuation characteristics into the heat conduction model. Based on the preset anisotropic conduction parameters in the heat conduction model, decompose the periodic fluctuation characteristics into heat flow vectors in the three-dimensional spatial coordinate system to obtain the heat flow attenuation coefficients in different conduction directions.

[0144] In step 502, heat flux vector decomposition refers to the process of decomposing the temperature field fluctuation characteristics according to the spatial conduction direction. The heat flux attenuation coefficient represents the degree of heat loss in different conduction directions.

[0145] In this embodiment, a three-dimensional thermal conductivity coordinate system is established based on the anisotropic characteristics of biological tissue. The extracted periodic fluctuation features are projected onto the three principal axes of the coordinate system. The thermal flux components in each axis are obtained by solving the thermal conductivity differential equation. Then, the thermal flux attenuation coefficient in each direction is calculated based on the dielectric properties of the tissue.

[0146] Step 503: Perform backpropagation iterative calculation on the extreme points of the temperature gradient in the periodic fluctuation characteristics through the constraint equations corresponding to the heat conduction model, and output the heat source diffusion rate. The constraint equations are used to characterize the nonlinear relationship between the heat flow attenuation coefficient and the distribution characteristics of the conduction medium.

[0147] In step 503, the extreme point of the temperature gradient refers to the spatial location point with the largest rate of temperature change in the dynamic change data of the temperature field on the surface of the organism. It originates from the temperature gradient abrupt change region identified after performing three-dimensional heat flow vector decomposition on the periodic fluctuation characteristics. The constraint equation set is a set of mathematical equations describing the relationship between heat flow attenuation and tissue characteristics, and its specific expression is:

[0148] Where Qx, Qy, Qz represent the heat flux in the x / y / z directions of the three-dimensional spatial coordinate system (unit: W / m). 2 αx, αy, αz represent anisotropic conduction parameters (corresponding to the heat flux attenuation coefficients in the claims, dimensionless). T represents the instantaneous value of the surface temperature field of the organism (unit: K). β represents the thermal diffusion compensation coefficient (unit: m). 2 / s). ρ represents the local density of biological tissue (unit: kg / m³). 3Cp represents the specific heat capacity of biological tissue (unit: J / (kg·K)). Tm represents the average temperature at the point where the heat source diffusion rate is calculated (unit: K). Backpropagation iteration refers to the reverse calculation process from the extreme temperature point on the body surface towards the heat source inside the body.

[0149] In this embodiment, the extreme point of the temperature gradient is used as the boundary condition, and the heat flow attenuation coefficient is used as the intermediate variable. The heat conduction equation is solved iteratively by the finite element method. The location and intensity parameters of the heat source are adjusted in each iteration until the calculation result matches the measured temperature field. Finally, a converged heat source diffusion rate distribution map is output.

[0150] Here is a specific example:

[0151] In the specific implementation of shoulder periarthritis treatment, the system first acquires dynamic temperature data from 48 temperature measurement points on the patient's shoulder using a 6×8 temperature array, forming a temperature field frame sequence with a time interval of 5 seconds. From this data, typical temperature fluctuation characteristics with a period of approximately 18 minutes are extracted. This period value is determined by the spectral peak obtained through fast Fourier transform analysis of 30 consecutive frames of temperature data. These periodic features are input into a heat conduction model considering the anisotropic properties of muscles, fascia, and other tissues. In the established three-dimensional coordinate system, the direction along the muscle fiber is set as the X-axis, the perpendicular direction to the muscle fiber as the Y-axis, and the depth direction as the Z-axis. According to the pre-calibrated anisotropic conduction parameters, the ratio of the thermal conductivity of muscle tissue in the X / Y / Z directions is 1.2:0.8:1.0. By solving the heat conduction equation and performing heat flux vector decomposition, the heat flux attenuation coefficients in the three directions are calculated to be 0.75, 0.85, and 0.90, respectively. These coefficient values ​​are obtained by normalizing the reciprocals of the thermal conductivity in each direction. Two extreme temperature gradient points near the scapula were detected as boundary conditions. Their coordinates were determined by finding the maximum value of the second derivative of the temperature field in space, and substituted into the constraint equations for inverse iterative calculation. After 15 iterations, the system converged and identified a major heat source region on the medial side of the scapula, with a calculated diffusion rate of 0.12℃ / mm. 2 ·s, this value is obtained by dividing the rate of change of heat flux at the final iteration step by the temperature gradient.

[0152] In this embodiment, through multi-dimensional feature extraction and precise reverse iterative calculation, the accurate inversion from the body surface temperature field to the internal heat source diffusion characteristics is achieved, providing a reliable thermodynamic parameter basis for subsequent targeted energy therapy and improving the targeting and effectiveness of the treatment.

[0153] To further improve the accuracy of generating resonant energy distribution features, in some embodiments, step 103: synchronously associating the dynamic change data with the heat source diffusion rate to generate resonant energy distribution features includes:

[0154] Step 601: Establish the phase delay mapping relationship between the temperature gradient corresponding to the dynamically changing data and the heat source diffusion rate.

[0155] In step 601, the phase delay mapping relationship refers to the time difference quantification relationship between the temperature gradient change and the heat source diffusion rate change, reflecting the time lag characteristics of the heat conduction process.

[0156] In this embodiment of the application, the phase difference spectrum between the temperature gradient time series and the heat source diffusion rate time series is determined by calculating the cross-correlation function between them. Then, the phase delay characteristics of each frequency band are extracted by Hilbert transform, and finally, the time delay mapping matrix of temperature gradient-heat source rate is established.

[0157] Step 602: Identify the temperature gradient corresponding to the abrupt change point of the heat source diffusion rate through the phase delay mapping relationship.

[0158] In step 602, the abrupt change point of the heat source diffusion rate originates from the calculated heat source diffusion rate. Its relationship with the "temperature gradient extremum" is as follows: the temperature gradient extremum corresponds to the abrupt change point of the heat source diffusion rate in the reverse calculation of the heat conduction model. The temperature gradient refers to the surface temperature change characteristic that has a causal relationship with the abrupt change point.

[0159] In this embodiment, the location of abrupt change is detected by performing differential operation on the heat source diffusion rate curve, and the corresponding temperature gradient change time is found by backtracking according to the phase delay mapping matrix. The temperature gradient features within a specific time window before and after that time are extracted as an associated feature set.

[0160] Step 603: Based on the temperature gradient corresponding to the mutation point, determine the temperature gradient phase-sensitive region where the mutation point is located, and spatially superimpose the heat conduction path of the temperature gradient phase-sensitive region with the propagation path of the biological wave to generate resonant energy distribution characteristics.

[0161] In step 603, the temperature gradient phase-sensitive region refers to the body surface region that responds most strongly to sudden changes in heat source intensity. The heat conduction path of the temperature gradient phase-sensitive region is an automatically generated conduction channel based on the spatial distribution relationship between anisotropic conduction parameters and extreme points of the temperature gradient in the heat conduction model. The propagation path of biological waves is the main channel for wave energy transfer determined by the inherent resonance characteristics of the organism, and its direction is determined by the characteristics of the organism's tissue structure.

[0162] In this embodiment of the application, the identified temperature gradient correlation feature set is subjected to spatial clustering analysis to determine the boundary of the sensitive area, and then the heat flow path network of the area is reconstructed through the heat conduction model; at the same time, the main path features of bio-wave propagation are extracted, the two path networks are spatially superimposed, and the energy coupling strength is marked at the intersection node to generate a resonant energy distribution feature map with spatial resolution.

[0163] Here is a specific example:

[0164] During the treatment of frozen shoulder, the system first uses shoulder temperature field data collected by a 6×8 temperature measurement array to calculate the temperature gradient distribution by calculating the temperature difference between adjacent measurement points. Simultaneously, based on the heat source diffusion rate data output by the heat conduction model, a phase delay mapping relationship is established between the two. Specifically, the calculation process involves cross-correlation analysis of the temperature gradient time series G(t) and the heat source diffusion rate time series S(t), obtaining a maximum cross-correlation coefficient of 0.85 corresponding to a time delay of 8 seconds, i.e., a phase delay mapping relationship of Δt = 8 seconds. When a sudden change in the heat source diffusion rate near the scapula is detected at time t0, with a change rate reaching 0.25℃ / mm... 2 ·s 2 Based on the phase delay relationship, the temperature gradient distribution at time t0+8 was traced back to identify three extreme temperature gradient points in the posterior deltoid region, with gradient values ​​of 1.2℃ / cm, 1.0℃ / cm, and 0.9℃ / cm, respectively. Using a heat conduction path tracing algorithm, the heat conduction paths corresponding to these three extreme points were determined to be mainly distributed along the muscle fiber direction, with a path width of approximately 2cm. Simultaneously, the bio-wave propagation path obtained through surface electromyography (EMG) signal detection showed three intersection nodes with the heat conduction path in the inferior angle of the scapular spine region. The energy coupling coefficient at these nodes was calculated using the formula E_c=α·G·S, where α is the tissue coupling coefficient (taken as 0.7), G is the temperature gradient, and S is the heat source intensity. The calculated coupling coefficients for the three nodes were 0.84, 0.70, and 0.63, respectively. The final generated resonant energy distribution feature map clearly marked these three key therapeutic target areas.

[0165] In the embodiments of this application, by establishing a precise phase delay mapping and path fusion mechanism, a deep coupling between thermodynamic properties and biowave properties is achieved, so that the generated resonant energy distribution characteristics can not only reflect the dynamic changes of the heat source, but also match the propagation law of biowaves, providing a reliable spatial positioning basis for targeted energy therapy.

[0166] To further improve the accuracy and real-time performance of targeted energy stimulation, in some embodiments, step 105: the targeted energy stimulation of the biological surface using the energy output sequence to obtain the targeted energy stimulation result includes:

[0167] Step 701: Generate an energy pulse sequence that matches the spatial distribution of the temperature field on the surface of the organism, based on the waveform duty cycle adjustment factor in the energy output sequence.

[0168] In step 701, the waveform duty cycle adjustment factor is used to control the duration and interval ratio of each pulse in the energy pulse sequence, and the frequency band switching time window is used to specify the switching time points between different biological wave frequency bands in the energy pulse sequence. Together, they define the time-domain waveform structure and frequency band switching sequence of the energy output sequence. The waveform duty cycle adjustment factor is an adjustment parameter that controls the ratio of the effective duration of the energy pulse to the total period. The energy pulse sequence refers to a combination of energy action units arranged according to a specific spatiotemporal pattern.

[0169] In this embodiment, the duty cycle encoding information in the energy output sequence is parsed, and combined with the spatial distribution characteristics of the temperature field, a pulse waveform matching the temperature distribution of each target area is generated by pulse width modulation technology, ensuring that the high temperature region corresponds to a shorter pulse width and the low temperature region corresponds to a longer pulse width.

[0170] Step 702: Based on the frequency band switching time window in the energy output sequence, embed a phase synchronization marker point corresponding to the boundary frequency of the biological wave's inherent frequency band in the energy pulse sequence.

[0171] In step 702, the frequency band switching time window refers to the time interval during which energy frequency adjustments are allowed. The phase synchronization marker point refers to the timing reference point used to align the bio-wave characteristics.

[0172] In this embodiment, special waveform markers are inserted at key time points of the energy pulse sequence based on the upper and lower boundary frequencies of the inherent frequency band of the biowave. The time interval between these marker points is dynamically adjusted according to the frequency band switching requirements to ensure that the energy frequency change is synchronized with the biowave characteristics.

[0173] Step 703: Guide the emission timing of the energy pulse sequence through the phase synchronization marker so that the energy density peak of the energy pulse sequence covers the abrupt change point of the heat source diffusion rate.

[0174] In step 703, the peak energy density refers to the maximum energy intensity value output by the periodic energy pulse sequence per unit time, which is calculated and generated by the waveform duty cycle adjustment factor and the intensity requirement of the heat source diffusion rate abrupt change point output by the heat conduction model. Emission timing control refers to the process of precisely controlling the timing of energy pulse emission.

[0175] In this embodiment, a phase synchronization marker is used as a time reference to calculate the optimal emission time for the energy pulse to reach the heat source abrupt change point. A delayed triggering mechanism is used to ensure that the peak energy density coincides precisely with the peak time of heat source activity.

[0176] Step 704: During the energy stimulation process, when the spatiotemporal distribution of the heat conduction rate and the peak energy density is detected to deviate from the preset correlation curve, the embedding position of the phase synchronization marker is dynamically adjusted according to the heat conduction rate feedback data of the energy pulse sequence's action area, so as to recalibrate the emission timing of the energy pulse sequence.

[0177] In step 704, the energy stimulation process is an automatic directed energy stimulation of the biological surface by a periodic energy pulse sequence through the adjustment of its phase synchronization control point. The preset correlation curve refers to the ideal correspondence between the heat conduction rate and the peak energy density. The spatiotemporal distribution of the peak energy density specifically refers to the distribution pattern of the energy pulse sequence reaching its maximum energy intensity in both spatial and temporal dimensions, reflecting the correspondence between changes in the heat conduction rate and the peak energy intensity in spatiotemporal coordinates. The spatiotemporal distribution of the peak energy density is the extreme value characteristic of the energy density distribution in the temporal and spatial domains; the former focuses on the spatiotemporal variation of the peak intensity, while the latter encompasses the intensity distribution characteristics of the entire energy field. They are related as one containing the other. The energy pulse sequence's action area refers to the specific part of the biological surface that needs to receive targeted energy stimulation, determined by the spatial coordinates of the temperature gradient phase-sensitive region identified in the heat conduction model. The relationship between the heat conduction rate and the heat source diffusion rate: The heat conduction rate is the specific manifestation of the heat source diffusion rate in the biological surface temperature field; the two are quantitatively converted through the anisotropic conduction parameters in the heat conduction model. Dynamic adjustment refers to the process of correcting control parameters based on real-time feedback.

[0178] In this embodiment, the thermal conductivity rate of the target area is monitored in real time. When the spatiotemporal correspondence between the thermal conductivity rate and the energy density peak is detected to deviate from the preset curve, the optimal position of the phase synchronization marker is immediately recalculated, and the emission timing of subsequent pulses is adjusted through parameter update instructions.

[0179] Step 705: Apply the time-corrected energy pulse sequence to the surface of the organism to generate a targeted energy stimulation result.

[0180] In step 705, timing correction refers to the calibration process for the energy pulse emission timing.

[0181] In this embodiment, the energy pulse sequence that has been adjusted in real time is output through a multi-channel transmitter. Each channel focuses energy according to the assigned target area coordinates and records the changes in physiological parameters of the target area, generating a stimulation result report that includes an assessment of the therapeutic effect.

[0182] Here is a specific example:

[0183] During the treatment of frozen shoulder, the system first generates a basic pulse sequence based on a 35% duty cycle adjustment factor in the energy output sequence. This duty cycle value is adjusted from an initial 20% based on an increase of 0.15℃ / s in the target temperature fluctuation rate. Based on the spatial temperature distribution data obtained from a 6×8 temperature measurement array in the shoulder area, the pulse energy intensity is allocated according to the temperature gradient ratio, with a distribution coefficient of 1.2 for high-temperature regions and 0.8 for low-temperature regions, ensuring that the energy distribution matches the temperature field. In the 580kHz fundamental frequency pulse sequence, a phase synchronization marker corresponding to the 550kHz boundary of the biological wave's inherent frequency band is embedded every 5 pulse cycles. The marker interval is calculated using the formula T = 1 / (f_high - f_low), where f_high is 600kHz and f_low is 550kHz. Real-time monitoring shows that the heat source diffusion rate in the scapular target area reaches 0.25℃ / mm at time t0. 2 When t0 changes abruptly, the system immediately activates the next phase synchronization marker closest to t0, triggering energy pulse emission and achieving a peak energy density of 3.5 W / cm². 2 The timing of the pulses precisely covers the moment of thermal abrupt change. During treatment, when a change in the thermal conduction rate in the posterior deltoid target area is detected 0.5 seconds earlier, the system embeds the corresponding phase synchronization marker point of that target area 0.5 seconds earlier. The recalibrated pulse sequence increases the correlation coefficient between the energy density peak and the spatiotemporal matching of thermal activity from 0.75 to 0.92. After 20 minutes of energy stimulation with timing correction, the inflammatory indicators in the three target areas decreased by an average of 40%, the patient's shoulder abduction angle improved from 60 degrees to 90 degrees, and the pain score decreased from 6 to 2, demonstrating the therapeutic effect.

[0184] In this embodiment, precise timing control and real-time feedback adjustment achieve millisecond-level synchronization between energy stimulation and the dynamic characteristics of the organism, ensuring both accurate delivery of therapeutic energy and adaptation to real-time changes in the organism's state, thus optimizing the therapeutic effect.

[0185] Figure 2 A schematic diagram of a bio-information targeted processing system based on bio-wave resonance dynamic sensing, provided as an embodiment of this application, is shown below. Figure 2 As shown, the system includes:

[0186] The acquisition module 21 is used to acquire dynamic change data of the temperature field on the surface of organisms.

[0187] The calculation module 22 is used to calculate the heat source diffusion rate inside the birth object based on the dynamic change data and in combination with the heat conduction model.

[0188] The generation module 23 is used to synchronously correlate and model the dynamic change data with the heat source diffusion rate to generate resonant energy distribution characteristics.

[0189] The matching module 24 is used to match the rate of change of thermodynamic fluctuation amplitude in the resonance energy distribution characteristics with the offset of biological wave resonant frequency in real time to generate an energy output sequence.

[0190] The stimulation module 25 is used to perform targeted energy stimulation on the surface of the organism through the energy output sequence to obtain the targeted energy stimulation result. The energy density distribution of the targeted energy stimulation result follows the heat source diffusion rate in real time, and the resonant frequency band of the energy density distribution dynamically covers the inherent frequency band of the biological wave, so as to realize the closed-loop targeted regulation of the phase delay of the biological wave and the dynamic coupling of thermodynamic parameters.

[0191] Figure 2 The aforementioned bio-information targeted processing system based on bio-wave resonance dynamic sensing can perform... Figure 1 The implementation principle and technical effects of the bio-information targeted processing method based on bio-wave resonance dynamic sensing described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the bio-information targeted processing system based on bio-wave resonance dynamic sensing in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0192] In one possible design, Figure 2 The bio-information targeting processing system based on bio-wave resonance dynamic sensing, as shown in the embodiment, can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0193] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0194] The processing component 32 is used to perform the above. Figure 1 The embodiment describes a bio-information targeted processing method based on bio-wave resonance dynamic sensing.

[0195] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0196] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0197] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0198] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0199] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0200] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0201] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown is a bio-information targeted processing method based on bio-wave resonance dynamic sensing.

[0202] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0203] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0204] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0205] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A bio-information targeted processing system based on bio-wave resonance dynamic sensing, characterized in that, include: The acquisition module is used to acquire dynamic change data of the temperature field on the surface of organisms; The calculation module is used to calculate the heat source diffusion rate inside the newborn object based on the dynamically changing data and in conjunction with the heat conduction model. The generation module is used to synchronously correlate and model the dynamic change data with the heat source diffusion rate to generate resonant energy distribution characteristics. The matching module is used to match the rate of change of thermodynamic fluctuation amplitude in the resonant energy distribution characteristics with the bio-wave resonant frequency offset in real time to generate an energy output sequence. The stimulation module is used to perform targeted energy stimulation on the surface of the organism through the energy output sequence to obtain the targeted energy stimulation result. The energy density distribution of the targeted energy stimulation result follows the heat source diffusion rate in real time, and the resonant frequency band of the energy density distribution dynamically covers the inherent frequency band of the biological wave, so as to realize the closed-loop targeted regulation of the dynamic coupling of biological wave phase delay and thermodynamic parameters. The step of matching the rate of change of thermodynamic fluctuation amplitude in the resonant energy distribution characteristics with the bio-wave resonant frequency offset in real time to generate an energy output sequence includes: Establish a dynamic correspondence between the rate of change of thermodynamic fluctuation amplitude and the offset of biological wave resonant frequency in the aforementioned resonance energy distribution characteristics; The heat source diffusion rate is converted into a biological wave band offset compensation parameter through the dynamic correspondence. A coupled analysis is performed on the bio-wave frequency band offset compensation parameters and the rate of change of thermodynamic fluctuation amplitude to generate an energy output sequence. The coupled analysis of the bio-wave frequency band offset compensation parameters and the rate of change of thermodynamic fluctuation amplitude to generate an energy output sequence includes: The bio-wave frequency band offset compensation parameter is decomposed into a frequency band baseline component and a frequency band offset component, and the thermodynamic fluctuation amplitude change rate is decomposed into a steady-state component and a dynamic fluctuation component. The frequency band baseline component and the steady-state component are coupled by energy intensity to form a basic waveform parameter set. At the same time, the frequency band offset component and the dynamic fluctuation component are phase correlated to form a dynamic modulation parameter set. Based on the aforementioned basic waveform parameter set, an energy reference frequency and duty cycle range are generated, while based on the aforementioned dynamic modulation parameter set, a frequency offset and duty cycle adjustment gradient are generated. Based on the energy reference frequency and the duty cycle range, initial waveform parameters are generated, and the frequency offset and the duty cycle adjustment gradient are superimposed on the initial waveform parameters as corresponding dynamic modulation quantities to generate target waveform parameters. Based on the time-varying characteristics of the rate of change of the thermodynamic fluctuation amplitude, the dynamic modulation amount is dynamically applied to the target waveform parameters to generate waveform modulation parameters; The energy emission device is driven by the waveform modulation parameters to generate an energy output sequence.

2. The system according to claim 1, characterized in that, The process of coupling the frequency band baseline component with the steady-state component using energy intensity to form a basic waveform parameter set, and simultaneously performing phase correlation processing on the frequency band offset component and the dynamic fluctuation component to form a dynamic modulation parameter set, includes: The baseline component of the frequency band is decomposed into a reference frequency component and a reference amplitude component, and the steady-state component is decomposed into a thermodynamic steady-state intensity component and a thermodynamic steady-state time component; The product of the reference amplitude component and the thermodynamic steady-state intensity component is used as the energy intensity reference value. At the same time, the reference frequency component and the thermodynamic steady-state time component are weighted and synthesized to generate waveform time reference parameters. The frequency band offset component is decomposed into a frequency shift direction component and a frequency shift amplitude component, and the dynamic fluctuation component is decomposed into a thermodynamic fluctuation direction component and a thermodynamic fluctuation amplitude component. A phase correlation index is established by the correspondence between the frequency shift direction component and the thermodynamic wave direction component in the time dimension, and the frequency shift amplitude component and the thermodynamic wave amplitude component are scaled proportionally to generate dynamic modulation amplitude parameters. The energy intensity reference value and the waveform time reference parameter are combined to form a basic waveform parameter set, and the phase correlation index and the dynamic modulation amplitude parameter are combined to form a dynamic modulation parameter set.

3. The system according to claim 1, characterized in that, The calculation of the heat source diffusion rate inside the newborn object based on the dynamically changing data and a heat conduction model includes: Extract periodic fluctuation features corresponding to the temperature field on the surface of the organism from the dynamic change data; The periodic fluctuation characteristics are input into the heat conduction model. Based on the anisotropic conduction parameters preset in the heat conduction model, the periodic fluctuation characteristics are decomposed into heat flow vectors in the three-dimensional spatial coordinate system to obtain the heat flow attenuation coefficients in different conduction directions. The extreme points of the temperature gradient in the periodic fluctuation characteristics are calculated by backpropagation using the constraint equations corresponding to the heat conduction model, and the heat source diffusion rate is output. The constraint equations are used to characterize the nonlinear relationship between the heat flow attenuation coefficient and the distribution characteristics of the conduction medium.

4. The system according to claim 1, characterized in that, The step of synchronously correlating and modeling the dynamic change data with the heat source diffusion rate to generate resonant energy distribution characteristics includes: Establish a phase delay mapping relationship between the temperature gradient corresponding to the dynamically changing data and the heat source diffusion rate; The temperature gradient corresponding to the abrupt change in the heat source diffusion rate is identified by the phase delay mapping relationship. Based on the temperature gradient corresponding to the mutation point, the temperature gradient phase-sensitive region where the mutation point is located is determined. The heat conduction path of the temperature gradient phase-sensitive region is spatially superimposed with the propagation path of the biological wave to generate resonant energy distribution characteristics.

5. The system according to claim 1, characterized in that, The process of applying targeted energy stimulation to the surface of the organism using the energy output sequence to obtain the targeted energy stimulation result includes: Based on the waveform duty cycle adjustment factor in the energy output sequence, an energy pulse sequence that matches the spatial distribution of the temperature field on the surface of the organism is generated. Based on the frequency band switching time window in the energy output sequence, a phase synchronization marker point corresponding to the boundary frequency of the inherent frequency band of the biological wave is embedded in the energy pulse sequence; The phase synchronization markers guide the emission timing of the energy pulse sequence so that the peak energy density of the energy pulse sequence covers the abrupt change in the heat source diffusion rate. During energy stimulation, when the spatiotemporal distribution of the heat conduction rate and the peak energy density is detected to deviate from the preset correlation curve, the embedding position of the phase synchronization marker is dynamically adjusted based on the heat conduction rate feedback data of the energy pulse sequence's action area to recalibrate the emission timing of the energy pulse sequence. The time-corrected energy pulse sequence is applied to the surface of the organism to generate a targeted energy stimulation result.

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