An energy uniform infiltration method and system based on multi-physical field interference cancellation

Abstract

This invention relates to the field of data processing technology and discloses a method and system for uniform energy penetration based on multi-physics field interference cancellation. The method includes: acquiring bio-feature data of a target region and generating a dataset; extracting initial energy parameters of the physical fields, determining the interference cancellation phase difference and amplitude compensation coefficient, and performing pre-compensation processing; jointly optimizing the pre-compensated parameters to generate a coordinated control command sequence; collecting physiological feedback parameters in real time through a biofeedback sensor to form a real-time dataset, comparing it with a preset target physiological response curve, and adjusting the frequency, intensity, and duty cycle according to the deviation to update the energy parameters; comparing the updated parameters with the energy uniformity convergence condition: if it is not satisfied, it is used as a new pre-compensated parameter, and the process returns to the joint optimization step to continue iterating; if it is satisfied, the current control command sequence is output as the final control command. This invention can improve the efficiency of uniform energy penetration based on multi-physics field interference cancellation.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for uniform energy penetration based on multi-physics field interference cancellation. Background Technology

[0002] Existing technologies lack standardized datasets tailored to the biological characteristics of the target region, making it impossible to accurately extract initial energy parameters from multiple physics fields. Furthermore, they fail to establish mechanisms for compensating for phase differences and amplitude discrepancies caused by interference between physics fields, resulting in a lack of targeted pre-compensation processing for energy parameters. This leads to poor compatibility between energy parameters and biological tissues, hindering stable and efficient uniform energy penetration control. Simultaneously, existing solutions do not incorporate biological characteristics to achieve coordinated matching of multiple physics field parameters, resulting in a lack of targeted energy output and failing to achieve ideal levels of penetration uniformity and depth of action.

[0003] Existing technologies have significant shortcomings in the optimization and feedback stages of multi-physics field energy penetration. They fail to jointly optimize pre-compensation parameters to generate coordinated control commands, thus failing to achieve temporal and phase coordination of multi-field energy. Furthermore, they lack real-time monitoring and dynamic correction mechanisms for biofeedback, cannot adjust the frequency, intensity, and duty cycle of the field based on physiological responses, and have not established a process for judging energy uniformity convergence and iterative optimization. Ultimately, this results in low energy penetration efficiency and unstable effects, making it difficult to meet the requirements for precise, uniform, and efficient physical energy intervention. Summary of the Invention

[0004] This invention provides a method and system for uniform energy penetration based on multi-physics field interference cancellation. It solves the problems of uneven energy penetration, poor adaptability, delayed response, and insufficient depth of action in existing technologies through six core mechanisms: multi-field source synergistic excitation, molecular-level energy matching, quantum tunneling and epigenetic regulation, bioelectromagnetic field harmonious resonance, magnetic moment reshaping and metabolic optimization, and dynamic adaptive feedback regulation.

[0005] To achieve the above objectives, the present invention provides an energy uniform penetration method based on multi-physics field interference destructive force, comprising: S1. Obtain biometric data of the target area, and generate a biometric dataset of the target area based on the biometric data; S2. Extract the initial energy parameters of the physical fields in the biometric dataset, and determine the interference destructive phase difference and amplitude compensation coefficient between the physical fields to perform pre-compensation processing on the initial energy parameters and generate the pre-compensated energy parameters of the initial energy parameters. S3. Perform joint optimization processing on the pre-compensated energy parameters to generate a cooperative control command sequence for the pre-compensated energy parameters; S4. In response to the coordinated control command sequence, physiological feedback parameters are collected in real time through a biofeedback sensor, and a real-time physiological feedback dataset of the physiological feedback parameters is generated based on the physiological feedback parameters. S5. Compare the deviation between the real-time physiological feedback dataset and the preset target physiological response curve, and determine the frequency adjustment, intensity adjustment and duty cycle adjustment of the physical field based on the deviation value, and generate the updated energy parameters of the physical field. S6. Compare the updated energy parameters with the preset energy uniformity convergence condition. If the preset energy uniformity convergence condition is not met, the updated energy parameters are used as the new pre-compensated energy parameters, and the process returns to S3. If the preset energy uniformity convergence condition is met, the current cooperative control instruction sequence is output as the final control instruction.

[0006] In a preferred embodiment, acquiring biometric data of the target region and generating a biometric dataset of the target region based on the biometric data includes: Raw signal data from sensors is received through a biometric acquisition interface. The raw signal data includes tissue impedance signals, microcirculation blood flow signals, and local temperature signals. The original signal data is subjected to outlier removal and data smoothing to obtain the preprocessed feature signal of the original signal data. The preprocessed feature signal is segmented according to a preset time window, and the signal amplitude within the time window is normalized to obtain the normalized biosignal segment of the preprocessed feature signal. The normalized biomarker signal fragments are encapsulated into structured data fields of the target region according to signal type, and the structured data fields are combined into a biomarker dataset of the target region.

[0007] In a preferred embodiment, the step of extracting the initial energy parameters of the physical fields in the biometric dataset and determining the destructive phase difference and amplitude compensation coefficient between the physical fields to pre-compensate the initial energy parameters and generate pre-compensated energy parameters includes: Physical field identification information is parsed from the biometric dataset, and the corresponding initial energy parameters are called according to the physical field identification information. The initial energy parameters include electromagnetic field frequency value, sound wave field phase value, far-infrared field intensity value, and photon radiation field duty cycle value. Based on the physical field identification information, read the interference cancellation phase difference and amplitude compensation coefficient between physical fields from the preset interference cancellation mapping relationship table; The phase value of the acoustic field is superimposed with the phase difference of the interference cancellation to obtain the adjusted phase value of the acoustic field. The electromagnetic field frequency value, the far-infrared field intensity value, and the photon radiation field duty cycle value are respectively subjected to amplitude weighting processing with the amplitude compensation coefficient to generate amplitude-adjusted parameter values ​​for the electromagnetic field frequency value, the far-infrared field intensity value, and the photon radiation field duty cycle value. The adjusted phase value and the adjusted amplitude parameter value are integrated to obtain the pre-compensated energy parameter of the initial energy parameter.

[0008] In a preferred embodiment, the step of jointly optimizing the pre-compensated energy parameters to generate a coordinated control command sequence for the pre-compensated energy parameters includes: Extract the electromagnetic field frequency pre-compensation value, acoustic wave field phase pre-compensation value, far-infrared field intensity pre-compensation value, and photon radiation field duty cycle pre-compensation value from the pre-compensated energy parameters respectively. The electromagnetic field frequency pre-compensation value, the far-infrared field intensity pre-compensation value, and the photon radiation field duty cycle pre-compensation value are time-aligned to generate the time-aligned energy parameter group of the pre-compensated energy parameters. The phase pre-compensation value of the acoustic field and the time-aligned energy parameter group are subjected to phase-amplitude joint matching processing to generate a joint matching energy parameter set of the acoustic field phase pre-compensation value and the time-aligned energy parameter group; The joint matching energy parameter set is encoded and converted to obtain the coordinated control command sequence of the pre-compensated energy parameters.

[0009] In a preferred embodiment, the step of responding to the coordinated control command sequence by acquiring physiological feedback parameters in real time via a biofeedback sensor and generating a real-time physiological feedback dataset based on the physiological feedback parameters includes: Receive the coordinated control command sequence and parse the acquisition start identifier and sensor channel configuration information in the coordinated control command sequence; Based on the acquisition start identifier and the sensor channel configuration information, the skin impedance sensor, microcirculation blood flow sensor and local temperature sensor in the biofeedback sensor group are activated to obtain the physiological feedback parameters of the sensors. The physiological feedback parameters are converted from analog to digital and marked with timestamps to obtain time-scaled physiological feedback parameters, which include changes in skin impedance, microcirculation blood flow acceleration, and temperature fluctuation amplitude. The time-stamped physiological feedback parameters are encapsulated according to the sampling channel and time order to obtain the real-time physiological feedback dataset of the time-stamped physiological feedback parameters.

[0010] In a preferred embodiment, the step of comparing the deviation between the real-time physiological feedback dataset and the preset target physiological response curve, and determining the frequency adjustment, intensity adjustment, and duty cycle adjustment of the physical field based on the deviation value, to generate the updated energy parameters of the physical field, includes: Read the time-stamped physiological data from the real-time physiological feedback dataset, and simultaneously read the pre-stored target physiological response curve, which contains a sequence of expected values ​​with the same parameter type as the time-stamped physiological data; The skin impedance change, microcirculation blood flow acceleration, and temperature fluctuation amplitude in the time-scaled physiological data are compared with the corresponding expected values ​​in the expected value sequence to obtain the skin impedance deviation value, microcirculation blood flow deviation value, and temperature fluctuation deviation value of the time-scaled physiological data. The comprehensive deviation coefficient of the time-scaled physiological data is calculated based on the skin impedance deviation value, microcirculation blood flow deviation value, and temperature fluctuation deviation value. Based on the sign and magnitude of the comprehensive deviation coefficient, the corresponding frequency adjustment, intensity adjustment, and duty cycle adjustment are retrieved from the preset adjustment amount mapping table. The frequency adjustment, intensity adjustment, and duty cycle adjustment are respectively superimposed onto the corresponding terms of the current energy parameters of the physical field to obtain the updated energy parameters of the physical field.

[0011] In a preferred embodiment, the formula for calculating the comprehensive deviation coefficient is: ; In the formula, The comprehensive deviation coefficient is... This is the first weighting coefficient corresponding to the skin impedance deviation value. This is the second weighting coefficient corresponding to the microcirculation blood flow deviation value. This is the third weighting coefficient corresponding to the temperature fluctuation deviation value. This refers to the skin impedance deviation value. This refers to the microcirculatory blood flow deviation value. This refers to the temperature fluctuation deviation value.

[0012] In a preferred embodiment, the updated energy parameters are compared with a preset energy uniformity convergence condition. If the preset energy uniformity convergence condition is not met, the updated energy parameters are used as the new pre-compensated energy parameters, and the process returns to S3. If the preset energy uniformity convergence condition is met, the current cooperative control command sequence is output as the final control command, including: The updated values ​​of electromagnetic field frequency, acoustic wave field phase, far-infrared field intensity, and photon radiation field duty cycle are extracted from the updated energy parameters. The updated values ​​of electromagnetic field frequency, far-infrared field intensity, and photon radiation field duty cycle are compared one by one with the frequency threshold, intensity threshold, and duty cycle threshold in the preset energy uniformity convergence condition to obtain the frequency compliance indicator, intensity compliance indicator, and duty cycle compliance indicator of the updated energy parameters. The frequency compliance indicator, the intensity compliance indicator, and the duty cycle compliance indicator are compared with a preset compliance threshold. When all three indicators are in compliance status, it is determined that the preset energy uniformity convergence condition is met; otherwise, it is determined that the preset energy uniformity convergence condition is not met. In response to the determination result that the preset energy uniformity convergence condition is met, the current cooperative control command sequence is marked as the final control command, and the final control command is output. In response to the determination result that the preset energy uniformity convergence condition is not met, the updated energy parameter is used as the new pre-compensated energy parameter, and the process returns to S3.

[0013] In a preferred embodiment, comparing the frequency compliance indicator, the intensity compliance indicator, and the duty cycle compliance indicator with a preset compliance threshold includes: The frequency compliance indicator, the intensity compliance indicator, and the duty cycle compliance indicator are compared with preset compliance thresholds. When the frequency compliance indicator is not less than the preset compliance threshold, the frequency parameter is determined to be in compliance status; When the strength compliance indicator is not less than the preset compliance threshold, the strength parameter is determined to be in compliance status; When the duty cycle compliance indicator is not less than the preset compliance threshold, the duty cycle parameter is determined to be in compliance status. The results of collecting the frequency parameter, intensity parameter, and duty cycle parameter are used to generate a compliance feedback signal for joint optimization processing.

[0014] To address the aforementioned problems, the present invention also provides an energy uniform penetration system based on multi-physics field interference destructive processes, the system comprising: The biometric module is used to acquire biometric data of the target area and generate a biometric dataset of the target area based on the biometric data. The pre-compensation module is used to extract the initial energy parameters of the physical fields in the biometric dataset, and determine the interference destructive phase difference and amplitude compensation coefficient between the physical fields, so as to perform pre-compensation processing on the initial energy parameters and generate the pre-compensated energy parameters of the initial energy parameters. The joint optimization module is used to perform joint optimization processing on the pre-compensated energy parameters to generate a coordinated control command sequence for the pre-compensated energy parameters; The feedback acquisition module is used to respond to the coordinated control command sequence, acquire physiological feedback parameters in real time through a biofeedback sensor, and generate a real-time physiological feedback dataset of the physiological feedback parameters based on the physiological feedback parameters. The deviation correction module is used to compare the deviation between the real-time physiological feedback dataset and the preset target physiological response curve, and determine the frequency adjustment, intensity adjustment and duty cycle adjustment of the physical field based on the deviation value, and generate the updated energy parameters of the physical field. The convergence output module is used to compare the updated energy parameters with the preset energy uniformity convergence condition. If the preset energy uniformity convergence condition is not met, the updated energy parameters are used as the new pre-compensated energy parameters and returned to the joint optimization module. If the preset energy uniformity convergence condition is met, the current cooperative control instruction sequence is output as the final control instruction.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention, through multi-source synergistic excitation and molecular-level energy matching, can form a highly spatiotemporally consistent energy coherence field, significantly improving the depth and uniformity of energy action. Simultaneously, it reconstructs water molecule clusters, activates mineral bioactivity, and comprehensively enhances material transport efficiency and nutrient bioavailability. Relying on quantum tunneling and epigenetic regulation mechanisms, it can overcome biological barriers to optimize gene expression. By leveraging the harmonious resonance of bioelectromagnetic fields to stabilize cell membrane potential, it effectively enhances intercellular communication efficiency and activates the body's self-repair and regulatory potential.

[0016] 2. This invention, relying on a dynamic adaptive feedback regulation and iterative optimization mechanism, can monitor physiological parameters in real time and dynamically adjust the physical field output to ensure that the energy effect is always within the optimal response range, achieving precise physical intervention. By reshaping and optimizing the enzyme activity conformation through magnetic moment remodeling, it accelerates energy synthesis and metabolic waste removal, comprehensively improving the basal metabolic level. Without the intervention of exogenous chemical substances, it achieves comprehensive health benefits such as promoting absorption, enhancing metabolism, detoxification and anti-aging, and repairing cells and tissues. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an energy uniform penetration method based on multi-physics field interference destructive, as provided in an embodiment of the present invention. Figure 2 A functional block diagram of an energy uniform penetration system based on multi-physics field interference cancellation is provided in an embodiment of the present invention. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides an energy uniform penetration method based on multi-physics field interference cancellation. The executing entity of this energy uniform penetration method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the energy uniform penetration method based on multi-physics field interference cancellation can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating an energy uniform penetration method based on multi-physics field interference destructive analysis according to an embodiment of the present invention. In this embodiment, the energy uniform penetration method based on multi-physics field interference destructive analysis includes: S1. Obtain biometric data of the target area, and generate a biometric dataset of the target area based on the biometric data; In this embodiment of the invention, acquiring biometric data of a target region and generating a biometric dataset of the target region based on the biometric data includes: Raw signal data from sensors is received through a biometric acquisition interface. The raw signal data includes tissue impedance signals, microcirculation blood flow signals, and local temperature signals. The original signal data is subjected to outlier removal and data smoothing to obtain the preprocessed feature signal of the original signal data. The preprocessed feature signal is segmented according to a preset time window, and the signal amplitude within the time window is normalized to obtain the normalized biosignal segment of the preprocessed feature signal. The normalized biomarker signal fragments are encapsulated into structured data fields of the target region according to signal type, and the structured data fields are combined into a biomarker dataset of the target region.

[0021] A data transmission channel is established between the biometric acquisition interface and various sensors. After the sensor continuously collects the corresponding physiological signals in the target area, it transmits the collected signals to the interface in the form of raw signal data. The interface receives the raw signal data completely. The raw signal data clearly includes three types of signal data: tissue impedance signal, microcirculation blood flow signal, and local temperature signal.

[0022] The entire original signal data is traversed, and each group of signal values ​​is compared with the preset normal signal value range. Signal values ​​that exceed the normal range are identified as abnormal values ​​and removed directly from the dataset. After the abnormal value removal is completed, a continuous sliding signal smoothing process is adopted to equalize the signal values ​​at adjacent positions in turn, eliminating random fluctuations and glitches in the signal. After the processing is completed, the preprocessed feature signal corresponding to the original signal data is obtained.

[0023] According to the pre-set time window length, starting from the beginning position of the pre-processed feature signal, the continuous signal segments are sequentially truncated until all pre-processed feature signals have been segmented. For each signal within the truncated time window, the signal amplitude is uniformly adjusted to the same numerical range to eliminate the influence of differences in the magnitude of different signal amplitudes. After processing, the normalized biosignal segment corresponding to the pre-processed feature signal is obtained.

[0024] Based on the signal type classification standards of tissue impedance signal, microcirculation blood flow signal, and local temperature signal, the normalized biomarker signal fragments are classified one by one. Each type of normalized biomarker signal fragment corresponds to an independent structured data field of the target region. All the classified structured data fields are integrated in a fixed combination order to finally form a complete biomarker dataset of the target region.

[0025] The beneficial effects are that by receiving three types of raw signal data through the biometric acquisition interface, the physiological characteristics of the target area can be fully captured, providing multi-dimensional and complete basic data support for subsequent energy parameter extraction and optimization.

[0026] Outlier removal and smoothing are performed on the raw signal data to effectively filter out interference signals and random fluctuations, ensuring the stability and reliability of the characteristic signals and providing high-quality input for data processing.

[0027] The feature signals are segmented and normalized according to a preset time window to eliminate differences in signal amplitude, making the features of different time periods comparable and facilitating the accurate extraction of physical field-related parameters.

[0028] Normalized signal segments are encapsulated into structured fields according to their type and combined into datasets, making the data structure standardized and orderly, facilitating the rapid parsing of physical field identifiers and initial energy parameters, and improving the efficiency of subsequent processing.

[0029] S2. Extract the initial energy parameters of the physical fields in the biometric dataset, and determine the interference destructive phase difference and amplitude compensation coefficient between the physical fields to perform pre-compensation processing on the initial energy parameters and generate the pre-compensated energy parameters of the initial energy parameters. In this embodiment of the invention, the step of extracting the initial energy parameters of the physical fields in the biometric dataset and determining the destructive phase difference and amplitude compensation coefficient between the physical fields to pre-compensate the initial energy parameters and generate pre-compensated energy parameters includes: Physical field identification information is parsed from the biometric dataset, and the corresponding initial energy parameters are called according to the physical field identification information. The initial energy parameters include electromagnetic field frequency value, sound wave field phase value, far-infrared field intensity value, and photon radiation field duty cycle value. Based on the physical field identification information, read the interference cancellation phase difference and amplitude compensation coefficient between physical fields from the preset interference cancellation mapping relationship table; The phase value of the acoustic field is superimposed with the phase difference of the interference cancellation to obtain the adjusted phase value of the acoustic field. The electromagnetic field frequency value, the far-infrared field intensity value, and the photon radiation field duty cycle value are respectively subjected to amplitude weighting processing with the amplitude compensation coefficient to generate amplitude-adjusted parameter values ​​for the electromagnetic field frequency value, the far-infrared field intensity value, and the photon radiation field duty cycle value. The adjusted phase value and the adjusted amplitude parameter value are integrated to obtain the pre-compensated energy parameter of the initial energy parameter.

[0030] It should be noted that the preset interference destructive mapping table is pre-constructed as follows: For different biological tissue types and their typical thickness ranges, finite element simulations or in vitro tissue experiments are used to measure the energy distribution generated by a single physical field within the tissue. Based on wave interference theory, the phase difference between the two physical fields is gradually adjusted through simulation software or experimental testing until interference destructive phenomena are observed at the target depth, i.e., the amplitude of the synthesized wave is significantly reduced. The phase difference between the two physical fields at this point is recorded as the interference destructive phase difference. Simultaneously, the energy attenuation ratio of each physical field involved in the interference before and after destructive phase difference is measured, and the amplitude compensation coefficient required to balance the energy contribution of each physical field in the target region is calculated accordingly. The above simulations or experiments cover multiple combinations of tissue parameters. The physical field identifier, interference destructive phase difference, and amplitude compensation coefficient corresponding to each combination are recorded, and these are summarized to construct a mapping table. In actual use, the system matches the closest combination of tissue parameters based on the biometric data of the current target region, thereby reading the corresponding interference destructive phase difference and amplitude compensation coefficient from the table.

[0031] The generated biometric dataset is parsed field by field to identify and extract the identification information used to distinguish different physical fields. Based on the identification information, the initial energy parameters that match it are located in the dataset. The initial energy parameters completely include four types of parameters: electromagnetic field frequency value, acoustic wave field phase value, far-infrared field intensity value, and photon radiation field duty cycle value.

[0032] Based on the physical field identification information obtained from the analysis, a precise search operation is performed in the pre-established and stored interference cancellation mapping relationship table. By matching the identification information with the corresponding relationship recorded in the table, the interference cancellation phase difference generated by the interaction between different physical fields and the amplitude compensation coefficient used to correct the signal amplitude are directly read.

[0033] The extracted acoustic wave field phase value is directly superimposed with the found interference destructive phase difference. The two sets of phase information are merged and calculated according to the phase change logic of signal propagation. After phase correction, the adjusted phase value corresponding to the acoustic wave field phase value is obtained.

[0034] The extracted electromagnetic field frequency value, far-infrared field intensity value, and photon radiation field duty cycle value are sequentially weighted with the read amplitude compensation coefficient. The original amplitude of each parameter is balanced and corrected based on the amplitude compensation coefficient, and the amplitude-adjusted parameter values ​​corresponding to the electromagnetic field frequency value, far-infrared field intensity value, and photon radiation field duty cycle value are generated respectively.

[0035] The adjusted phase value obtained after phase correction and the adjusted parameter value obtained after amplitude correction are systematically integrated according to the inherent structure of the physical field parameters, so that all kinds of parameters form a unified and complete parameter set, and finally the pre-compensated energy parameter corresponding to the initial energy parameter is obtained.

[0036] The beneficial effects are that by parsing the physical field identifiers from the biometric dataset and calling four types of initial energy parameters, the core control indicators of multiphysics fields can be accurately located, providing a clear and complete parameter basis for subsequent pre-compensation processing.

[0037] Based on the physical field identifier, the interference destructive phase difference and amplitude compensation coefficient are read from the preset mapping table to quickly obtain the adaptive compensation parameters, avoid blind adjustment, and improve the pertinence and efficiency of pre-compensation.

[0038] The adjusted phase value is obtained by superimposing the phase value of the sound wave field with the phase difference of the interference cancellation, which cancels the phase interference between physical fields and ensures the stability and accuracy of the sound wave field energy transmission.

[0039] By weighting the three types of energy parameters using an amplitude compensation coefficient, the energy contribution of each physical field is balanced, avoiding uneven energy penetration caused by an excessively high proportion of a single parameter.

[0040] The integrated and adjusted phase value and amplitude adjusted parameter value are combined to form the pre-compensated energy parameter, realizing the synergistic optimization of multi-physics field parameters and providing more adaptable parameter support for uniform energy penetration.

[0041] S3. Perform joint optimization processing on the pre-compensated energy parameters to generate a cooperative control command sequence for the pre-compensated energy parameters; In this embodiment of the invention, the step of performing joint optimization processing on the pre-compensated energy parameters to generate a cooperative control command sequence for the pre-compensated energy parameters includes: Extract the electromagnetic field frequency pre-compensation value, acoustic wave field phase pre-compensation value, far-infrared field intensity pre-compensation value, and photon radiation field duty cycle pre-compensation value from the pre-compensated energy parameters respectively. The electromagnetic field frequency pre-compensation value, the far-infrared field intensity pre-compensation value, and the photon radiation field duty cycle pre-compensation value are time-aligned to generate the time-aligned energy parameter group of the pre-compensated energy parameters. The phase pre-compensation value of the acoustic field and the time-aligned energy parameter group are subjected to phase-amplitude joint matching processing to generate a joint matching energy parameter set of the acoustic field phase pre-compensation value and the time-aligned energy parameter group; The joint matching energy parameter set is encoded and converted to obtain the coordinated control command sequence of the pre-compensated energy parameters.

[0042] The energy parameters obtained after pre-compensation processing are broken down and extracted one by one to extract the independent parameter contents, and the electromagnetic field frequency pre-compensation value, the acoustic wave field phase pre-compensation value, the far-infrared field intensity pre-compensation value, and the photon radiation field duty cycle pre-compensation value are completely extracted.

[0043] Using the time series of far-infrared field intensity pre-compensation values ​​as a reference, the delay times of the signal sequences of electromagnetic field frequency pre-compensation values ​​and photon radiation field duty cycle pre-compensation values ​​relative to this reference are calculated. A sliding window cross-correlation algorithm is employed, shifting the two signal sequences within a time window and calculating the correlation coefficient under different shifts. The shift amount at which the correlation coefficient is maximized is taken as the delay time. Then, the sequences of electromagnetic field frequency pre-compensation values ​​and photon radiation field duty cycle pre-compensation values ​​are shifted in the reverse direction according to the calculated delay time, ensuring complete alignment of the three sequences on the time axis, forming a time-aligned energy parameter set.

[0044] The extracted acoustic field phase pre-compensation value is synchronously matched with each parameter in the time-aligned energy parameter group. According to the principle of mutual adaptation between phase change law and amplitude change trend, the parameters are adjusted one by one to keep the phase change and amplitude change coordinated and consistent. After processing, a joint matching energy parameter set corresponding to the acoustic field phase pre-compensation value and the time-aligned energy parameter group is generated.

[0045] The energy parameters in the joint matching energy parameter set are converted bit by bit according to the instruction format that the equipment can recognize. The continuous energy parameter information is converted into discrete and ordered instruction content, so that the parameter information is converted into a form of control instruction that can be directly executed. After the conversion is completed, the coordinated control instruction sequence corresponding to the pre-compensated energy parameters is obtained.

[0046] The beneficial effects are that four types of core pre-compensation values ​​are extracted from the energy parameters after pre-compensation, and key control parameters of multiphysics fields are accurately separated, providing clear and independent optimization objects for subsequent joint optimization and ensuring the targeting of optimization.

[0047] The pre-compensation values ​​of the three types of energy parameters are time-series aligned to eliminate the time delay deviation of different parameters, keep the parameter changes synchronized, and lay the foundation for the synergistic effect of multiphysics.

[0048] By performing phase-amplitude joint matching between the acoustic field phase pre-compensation value and the timing alignment parameter group, the phase change and amplitude change are adapted and coordinated, thereby improving the coordination and accuracy of multi-physics energy superposition.

[0049] The joint matching energy parameter set is encoded and converted to generate a coordinated control command sequence. The optimized parameters are then converted into control commands that can be executed by the equipment, ensuring that the multiphysics field operates accurately according to the optimized scheme.

[0050] S4. In response to the coordinated control command sequence, physiological feedback parameters are collected in real time through a biofeedback sensor, and a real-time physiological feedback dataset of the physiological feedback parameters is generated based on the physiological feedback parameters. In this embodiment of the invention, the step of responding to the coordinated control command sequence by real-time acquisition of physiological feedback parameters through a biofeedback sensor and generating a real-time physiological feedback dataset based on the physiological feedback parameters includes: Receive the coordinated control command sequence and parse the acquisition start identifier and sensor channel configuration information in the coordinated control command sequence; Based on the acquisition start identifier and the sensor channel configuration information, the skin impedance sensor, microcirculation blood flow sensor and local temperature sensor in the biofeedback sensor group are activated to obtain the physiological feedback parameters of the sensors. The physiological feedback parameters are converted from analog to digital and marked with timestamps to obtain time-scaled physiological feedback parameters, which include changes in skin impedance, microcirculation blood flow acceleration, and temperature fluctuation amplitude. The time-stamped physiological feedback parameters are encapsulated according to the sampling channel and time order to obtain the real-time physiological feedback dataset of the time-stamped physiological feedback parameters.

[0051] It should be noted that the change in skin impedance refers to the difference between the current measured skin impedance value (unit: ohms) and the baseline impedance value (the expected value at the corresponding time point in the target physiological response curve), and the difference can be positive or negative; microcirculatory blood flow acceleration refers to the first derivative of microcirculatory blood flow velocity (unit: millimeters per second) with respect to time. The calculation formula is: acceleration equals the blood flow velocity at the current moment minus the blood flow velocity at the previous sampling moment, and then divided by the sampling interval (unit: seconds). The unit of the result is millimeters per square second; temperature fluctuation amplitude refers to the difference between the current local temperature (unit: degrees Celsius) and the absolute value of the temperature at the previous moment, and then divided by the resolution of the temperature sensor (0.1 degrees Celsius), to obtain the dimensionless fluctuation amplitude value.

[0052] The system receives the complete sequence of coordinated control commands and then analyzes the command content segment by segment. It accurately identifies and separates the acquisition start identifier used to initiate the acquisition process and the sensor channel configuration information used to allocate the sensor working path from the command sequence.

[0053] Based on the identified acquisition start flag, the sensor operation start logic is triggered. At the same time, according to the parsed sensor channel configuration information, the corresponding signal transmission channel is allocated, and the corresponding skin impedance sensor, microcirculation blood flow sensor and local temperature sensor inside the biofeedback sensor group are turned on in sequence. After each sensor is turned on, it continuously acquires the corresponding physiological signal and outputs the physiological feedback parameters of the sensor.

[0054] The physiological feedback parameters output by the sensor are transmitted to the signal conversion unit, which converts the continuous analog physiological signals into digital signals that can be recognized by the data processing unit. Then, the corresponding acquisition time information is labeled for each set of converted digital physiological signals. After processing, the time-scaled physiological feedback parameters corresponding to the physiological feedback parameters are obtained. These time-scaled physiological feedback parameters fully include the changes in skin impedance, microcirculatory blood flow acceleration, and temperature fluctuation amplitude.

[0055] The time-stamped physiological feedback parameters are categorized and organized according to the sampling channel category of each sensor, and then arranged in an orderly manner according to the time sequence of each parameter. The arranged time-stamped physiological feedback parameters are integrated and packaged into a data set in a unified format. After packaging, the real-time physiological feedback dataset corresponding to the time-stamped physiological feedback parameters is obtained.

[0056] The beneficial effects are that by receiving collaborative control commands and parsing the acquisition start identifier and channel configuration information, the sensor working commands can be accurately obtained, providing clear guidance for the acquisition of physiological feedback parameters and ensuring the orderly start of the acquisition work.

[0057] Based on the analytical information, three types of sensors are activated to collect physiological feedback parameters, comprehensively capturing physiological changes related to skin impedance, microcirculation blood flow, and temperature, providing multi-dimensional data support for subsequent deviation analysis.

[0058] The physiological feedback parameters are converted from analog to digital and timestamped to convert analog signals into digital signals and bind the acquisition time, ensuring that the parameters are traceable and easy to process, thereby improving data availability.

[0059] By encapsulating time-stamped physiological feedback parameters according to sampling channels and time sequence to generate datasets, the data structure becomes regular and the logic is clear, making it easier to compare with the target physiological response curve and improving the efficiency of deviation calculation.

[0060] S5. Compare the deviation between the real-time physiological feedback dataset and the preset target physiological response curve, and determine the frequency adjustment, intensity adjustment and duty cycle adjustment of the physical field based on the deviation value, and generate the updated energy parameters of the physical field. In this embodiment of the invention, the step of comparing the deviation between the real-time physiological feedback dataset and the preset target physiological response curve, and determining the frequency adjustment, intensity adjustment, and duty cycle adjustment of the physical field based on the deviation value, to generate the updated energy parameters of the physical field, includes: Read the time-stamped physiological data from the real-time physiological feedback dataset, and simultaneously read the pre-stored target physiological response curve, which contains a sequence of expected values ​​with the same parameter type as the time-stamped physiological data; The skin impedance change, microcirculation blood flow acceleration, and temperature fluctuation amplitude in the time-scaled physiological data are compared with the corresponding expected values ​​in the expected value sequence to obtain the skin impedance deviation value, microcirculation blood flow deviation value, and temperature fluctuation deviation value of the time-scaled physiological data. The comprehensive deviation coefficient of the time-scaled physiological data is calculated based on the skin impedance deviation value, microcirculation blood flow deviation value, and temperature fluctuation deviation value. Based on the sign and magnitude of the comprehensive deviation coefficient, the corresponding frequency adjustment, intensity adjustment, and duty cycle adjustment are retrieved from the preset adjustment amount mapping table. The frequency adjustment, intensity adjustment, and duty cycle adjustment are respectively superimposed onto the corresponding terms of the current energy parameters of the physical field to obtain the updated energy parameters of the physical field.

[0061] The formula for calculating the comprehensive deviation coefficient is as follows: ; In the formula, The comprehensive deviation coefficient is... This is the first weighting coefficient corresponding to the skin impedance deviation value. This is the second weighting coefficient corresponding to the microcirculation blood flow deviation value. This is the third weighting coefficient corresponding to the temperature fluctuation deviation value. This refers to the skin impedance deviation value. This refers to the microcirculatory blood flow deviation value. This refers to the temperature fluctuation deviation value.

[0062] It should be noted that the preset target physiological response curve is obtained by sampling the target area of ​​a large number of healthy people in a resting state without any external physical field applied, collecting data on changes in skin impedance, microcirculation blood flow and local temperature within a continuous time window, and after statistical averaging, obtaining a sequence of expected values ​​representing the normal physiological fluctuation range. This sequence is the general standard target physiological response curve.

[0063] The system reads all time-stamped physiological data in chronological order from the real-time physiological feedback dataset. At the same time, it retrieves the pre-set and saved target physiological response curve from the local storage area. The target physiological response curve contains a sequence of standard expected values ​​of parameters of the same type as the real-time collected time-stamped physiological data.

[0064] The changes in skin impedance in the time-stamped physiological data were directly compared with the expected values ​​of skin impedance in the expected value sequence. The microcirculation blood flow acceleration was directly compared with the expected values ​​of microcirculation blood flow in the expected value sequence. The temperature fluctuation amplitude was directly compared with the expected values ​​of temperature fluctuation in the expected value sequence. After comparison, the deviation values ​​of skin impedance, microcirculation blood flow, and temperature fluctuation corresponding to the time-stamped physiological data were obtained.

[0065] The obtained skin impedance deviation value, microcirculation blood flow deviation value, and temperature fluctuation deviation value are comprehensively calculated according to a unified weighting rule. The degree of deviation of the three types of deviation values ​​is judged as a whole, and the comprehensive deviation coefficient corresponding to the time-scaled physiological data is obtained after the overall judgment.

[0066] First, determine the positive or negative attribute of the comprehensive deviation coefficient, then determine the level to which the value of the comprehensive deviation coefficient belongs. Based on the positive or negative attribute and the level of the value, perform a step-by-step matching search in the pre-established adjustment amount mapping table, and find the frequency adjustment amount, intensity adjustment amount and duty cycle adjustment amount corresponding to the comprehensive deviation coefficient from the table.

[0067] The frequency adjustment value is directly superimposed with the frequency-related term in the current energy parameters of the physical field, the intensity adjustment value is directly superimposed with the intensity-related term in the current energy parameters of the physical field, and the duty cycle adjustment value is directly superimposed with the duty cycle-related term in the current energy parameters of the physical field. After the superposition is completed, the updated energy parameters of the physical field are obtained.

[0068] The skin impedance deviation value is obtained by actually measuring the skin impedance of the test subject, and then subtracting the preset standard skin impedance value from the actual skin impedance value. The result is the skin impedance deviation value.

[0069] The microcirculation blood flow deviation value is obtained by actually measuring the microcirculation blood flow velocity of the test object, obtaining the actual microcirculation blood flow velocity value, and then subtracting the preset standard microcirculation blood flow velocity value from the actual microcirculation blood flow velocity value. The result is the microcirculation blood flow deviation value.

[0070] The temperature fluctuation deviation value is calculated by continuously monitoring the skin temperature of the test subject, recording the actual skin temperature change data over a period of time, subtracting the actual skin temperature minimum value from the actual skin temperature maximum value during that period to obtain the actual temperature fluctuation value, and then subtracting the preset standard temperature fluctuation value from the actual temperature fluctuation value.

[0071] The first weighting coefficient is a specific value determined by expert scoring based on the degree of influence of the skin impedance deviation value on the comprehensive deviation coefficient. For example, when the skin impedance deviation value has the greatest influence on the comprehensive deviation coefficient, the first weighting coefficient is set to 0.5. This value is only used to reflect the importance of the skin impedance deviation value in the comprehensive evaluation and is not involved in other calculations.

[0072] The second weighting coefficient is a specific value determined by expert scoring based on the degree of influence of the microcirculation blood flow deviation value on the comprehensive deviation coefficient. For example, when the influence of the microcirculation blood flow deviation value on the comprehensive deviation coefficient is moderate, the second weighting coefficient is determined to be 0.3. This value is only used to reflect the importance of the microcirculation blood flow deviation value in the comprehensive evaluation and is not involved in other calculations.

[0073] The third weighting coefficient is a specific value determined by expert scoring based on the degree of influence of temperature fluctuation deviation on the comprehensive deviation coefficient. For example, when the influence of temperature fluctuation deviation on the comprehensive deviation coefficient is small, the third weighting coefficient is set to 0.2. This value is only used to reflect the importance of temperature fluctuation deviation in the comprehensive evaluation and is not involved in other calculations.

[0074] The formula for calculating the comprehensive deviation coefficient is used to calculate the weighted average of skin impedance deviation, microcirculation blood flow deviation, and temperature fluctuation deviation. The skin impedance deviation is multiplied by a first weighting coefficient, the microcirculation blood flow deviation by a second weighting coefficient, and the temperature fluctuation deviation by a third weighting coefficient. These three products are added together to obtain a sum, which is then divided by the sum of the first, second, and third weighting coefficients. The final result is the comprehensive deviation coefficient. This calculation process can comprehensively reflect the deviation of the three indicators of skin impedance, microcirculation blood flow, and temperature fluctuation, and realize the quantitative evaluation of the comprehensive deviation of the relevant detection indicators.

[0075] The beneficial effects are that reading real-time time-stamped physiological data and preset target physiological response curves ensures that the parameter types of the comparison objects are consistent, providing a unified benchmark for deviation analysis and ensuring the pertinence and rationality of subsequent adjustments.

[0076] By comparing the three types of physiological parameters with their corresponding expected values, deviation values ​​are obtained, accurately capturing the deviation of physiological responses from the target in each dimension, and providing comprehensive and accurate basic data for comprehensive deviation calculation.

[0077] The comprehensive deviation coefficient is calculated based on the three types of deviation values ​​to achieve the integration and quantification of multi-dimensional deviations, intuitively reflecting the degree of deviation of the overall physiological response, and providing a scientific basis for determining the adjustment amount.

[0078] By querying the corresponding adjustment amount based on the sign and level of the comprehensive deviation coefficient, the adjustment of frequency, intensity and duty cycle can be made to better match the actual deviation situation, thereby improving the accuracy and adaptability of parameter adjustment.

[0079] The three types of adjustment values ​​are superimposed on the corresponding items of the current energy parameters to achieve dynamic correction of the physical field parameters, so that the energy output is more adapted to physiological needs and the uniform energy penetration effect is guaranteed.

[0080] This formula introduces three types of weighting coefficients to sum the corresponding deviation values, which can highlight the differences in the influence of different physiological indicators on the overall deviation, making the calculation results more consistent with the actual physiological response patterns.

[0081] The formula integrates three core deviation values: skin impedance, microcirculation blood flow, and temperature fluctuation, to achieve quantitative fusion of multi-dimensional physiological deviations and provide a comprehensive basis for subsequent parameter adjustments.

[0082] Normalization calculations are performed using the sum of the three weighting coefficients as the denominator, limiting the comprehensive deviation coefficient to a reasonable range of values, thus ensuring that deviation assessments under different scenarios have a unified standard and comparability.

[0083] The formula is logically simple and clear, and the calculation process is simple and efficient. It can be quickly transformed into an engineering execution process, and efficiently output accurate and quantitative comprehensive deviation results, providing timely support for the dynamic adjustment of physical field parameters.

[0084] By quantifying the comprehensive impact of multi-dimensional physiological deviations through a weighted average method, the one-sidedness of judging by a single indicator is effectively avoided, and the scientificity and accuracy of adjusting physical field energy parameters are improved.

[0085] S6. Compare the updated energy parameters with the preset energy uniformity convergence condition. If the preset energy uniformity convergence condition is not met, the updated energy parameters are used as the new pre-compensated energy parameters, and the process returns to S3. If the preset energy uniformity convergence condition is met, the current cooperative control instruction sequence is output as the final control instruction.

[0086] In this embodiment of the invention, the updated energy parameters are compared with a preset energy uniformity convergence condition. If the preset energy uniformity convergence condition is not met, the updated energy parameters are used as new pre-compensated energy parameters, and the process returns to S3. If the preset energy uniformity convergence condition is met, the current cooperative control instruction sequence is output as the final control instruction, including: The updated values ​​of electromagnetic field frequency, acoustic wave field phase, far-infrared field intensity, and photon radiation field duty cycle are extracted from the updated energy parameters. The updated values ​​of electromagnetic field frequency, far-infrared field intensity, and photon radiation field duty cycle are compared one by one with the frequency threshold, intensity threshold, and duty cycle threshold in the preset energy uniformity convergence condition to obtain the frequency compliance indicator, intensity compliance indicator, and duty cycle compliance indicator of the updated energy parameters. The frequency compliance indicator, the intensity compliance indicator, and the duty cycle compliance indicator are compared with a preset compliance threshold. When all three indicators are in compliance status, it is determined that the preset energy uniformity convergence condition is met; otherwise, it is determined that the preset energy uniformity convergence condition is not met. In response to the determination result that the preset energy uniformity convergence condition is met, the current cooperative control command sequence is marked as the final control command, and the final control command is output. In response to the determination result that the preset energy uniformity convergence condition is not met, the updated energy parameter is used as the new pre-compensated energy parameter, and the process returns to S3.

[0087] The step of comparing the frequency compliance indicator, the intensity compliance indicator, and the duty cycle compliance indicator with preset compliance thresholds includes: The frequency compliance indicator, the intensity compliance indicator, and the duty cycle compliance indicator are compared with preset compliance thresholds. When the frequency compliance indicator is not less than the preset compliance threshold, the frequency parameter is determined to be in compliance status; When the strength compliance indicator is not less than the preset compliance threshold, the strength parameter is determined to be in compliance status; When the duty cycle compliance indicator is not less than the preset compliance threshold, the duty cycle parameter is determined to be in compliance status. The results of collecting the frequency parameter, intensity parameter, and duty cycle parameter are used to generate a compliance feedback signal for joint optimization processing.

[0088] It should be noted that the frequency threshold, intensity threshold, and duty cycle threshold in the preset energy uniformity convergence condition are set as follows: Frequency threshold: The deviation between the updated electromagnetic field frequency and the target frequency shall not exceed ±5%; Intensity threshold: The deviation between the updated far-infrared field intensity value and the target intensity shall not exceed ±3%; Duty cycle threshold: The deviation between the updated duty cycle value of the photon radiation field and the target duty cycle shall not exceed ±2%.

[0089] The preset compliance threshold is set to 0.8, meaning that when the frequency compliance indicator, intensity compliance indicator, and duty cycle compliance indicator are all greater than or equal to 0.8, it is considered compliant. The compliance indicator is calculated as follows: Compliance indicator = 1 - (absolute deviation value / threshold), with an upper limit of 1 and a lower limit of 0.

[0090] The generated updated energy parameters are disassembled and extracted one by one, and independent parameter items are completely separated from the updated energy parameters. The updated values ​​of electromagnetic field frequency, acoustic wave field phase, far-infrared field intensity, and photon radiation field duty cycle are accurately extracted to ensure that no update value is omitted or confused and that they completely correspond to the contents of the updated energy parameters.

[0091] The preset and stored energy uniformity convergence conditions are retrieved, and the frequency threshold, intensity threshold, and duty cycle threshold are separated from these conditions. The extracted electromagnetic field frequency update value is directly compared with the frequency threshold in the convergence conditions, the far-infrared field intensity update value is directly compared with the intensity threshold in the convergence conditions, and the photon radiation field duty cycle update value is directly compared with the duty cycle threshold in the convergence conditions. A corresponding compliance label is generated for each comparison, and the frequency compliance label, intensity compliance label, and duty cycle compliance label corresponding to the updated energy parameters are obtained respectively.

[0092] The system retrieves a pre-set threshold for compliance, which is used to determine whether various compliance indicators are in a qualified state. The obtained frequency compliance indicators, intensity compliance indicators, and duty cycle compliance indicators are compared with the pre-set thresholds one by one, and the state of each indicator is determined one by one. When all three indicators reach the compliance state corresponding to the pre-set threshold, it is directly determined that the updated energy parameter meets the pre-set energy uniformity convergence condition. If any indicator fails to reach the compliance state corresponding to the pre-set threshold, it is determined that the updated energy parameter does not meet the pre-set energy uniformity convergence condition.

[0093] When the determination result is that the preset energy uniformity convergence condition is met, the system immediately responds to the determination result, marks the currently executing collaborative control instruction sequence, and explicitly marks it as the final control instruction. After marking, the final control instruction is completely output according to the preset output path, ensuring that the output final control instruction is complete and error-free, and can be directly used for subsequent equipment control to achieve comprehensive health benefits such as promoting absorption, improving metabolism, detoxification and anti-aging, and optimizing gene expression.

[0094] When the judgment result is that the preset energy uniformity convergence condition is not met, the judgment result is responded to immediately. The updated energy parameter generated this time is directly used as the new pre-compensated energy parameter to replace the original pre-compensated energy parameter. After the replacement is completed, the process returns to step three according to the preset process path and starts the joint optimization process for the new pre-compensated energy parameter again until the generated updated energy parameter meets the preset energy uniformity convergence condition.

[0095] Retrieve the preset and stored target thresholds, and simultaneously extract the generated frequency target, intensity target, and duty cycle target indicators. Compare these three target indicators with the preset target thresholds simultaneously to ensure that each target indicator can be completely and accurately compared with the preset target thresholds without any omissions or comparison deviations.

[0096] After comparing the frequency compliance indicator with the preset compliance threshold, the comparison result is judged. When the value of the frequency compliance indicator is not less than the value of the preset compliance threshold, the frequency parameter in the updated energy parameters is directly judged to be in compliance state, indicating that the frequency parameter meets the frequency requirements in the preset energy uniformity convergence condition.

[0097] After comparing the strength compliance indicator with the preset compliance threshold, the comparison result is judged. When the value of the strength compliance indicator is not less than the value of the preset compliance threshold, the strength parameter in the updated energy parameters is directly judged to be in compliance state, and the strength parameter meets the requirements of the preset energy uniformity convergence condition regarding strength.

[0098] After comparing the duty cycle compliance indicator with the preset compliance threshold, the comparison result is judged. When the value of the duty cycle compliance indicator is not less than the value of the preset compliance threshold, the duty cycle parameter in the updated energy parameters is directly judged to be in compliance state, and it is clear that the duty cycle parameter meets the requirements of the preset energy uniformity convergence condition regarding the duty cycle.

[0099] The judgment results of frequency parameter, intensity parameter and duty cycle parameter are collected one by one. The qualified or unqualified status of the three parameters is fully summarized. After the summary is completed, a qualified judgment feedback signal is generated to provide feedback on the effect of joint optimization processing. The qualified judgment feedback signal fully contains the judgment results of the three parameters, and provides a direct basis for subsequent judgment on whether the preset energy uniformity convergence condition is met.

[0100] The beneficial effects are that four types of core updated values ​​are extracted from the updated energy parameters, accurately locking the key control parameters of the multiphysics field, providing a clear and focused analysis object for the convergence condition comparison, and ensuring the pertinence of the judgment.

[0101] The updated values ​​of the three types of energy parameters are compared with the corresponding thresholds one by one to generate compliance indicators, quantifying the degree of conformity of each parameter with the energy uniformity requirements, and providing an objective basis for comprehensive judgment.

[0102] By comprehensively comparing the three types of compliance indicators with preset thresholds, the convergence conditions are determined, and a multi-dimensional verification mechanism is established to ensure the rigor and accuracy of energy uniformity determination.

[0103] When the convergence condition is met, the final control command is marked and output, confirming that the energy penetration has achieved the expected effect and providing a stable and reliable control basis for subsequent energy output. When the convergence condition is not met, the updated parameters are returned as new pre-compensation parameters for iteration, constructing a closed-loop optimization mechanism to continuously correct the energy parameters to ensure uniform energy penetration.

[0104] By comparing the three types of compliance indicators with the preset compliance thresholds one by one, a unified and clear judgment standard is established to avoid the subjectivity of single-parameter judgment and provide an objective basis for judging the convergence of energy uniformity.

[0105] When the frequency compliance indicator is not less than a preset threshold, the frequency parameter is considered to meet the standard. This ensures precise control of the energy uniformity requirements of the electromagnetic field frequency and guarantees that the core parameters meet the penetration control standards. When the intensity compliance indicator is not less than a preset threshold, the intensity parameter is considered to meet the standard. This ensures strict verification of the stability of the far-infrared field intensity and guarantees that the energy penetration intensity meets the uniformity convergence condition.

[0106] The duty cycle parameter is deemed to meet the standard when the duty cycle indicator is not less than a preset threshold, effectively ensuring the rationality of the photon radiation field duty cycle and providing parameter support for uniform energy penetration. The results of the three types of parameter determinations are collected to generate a compliance determination feedback signal, comprehensively summarizing multi-dimensional determination information to provide clear data references for subsequent iterative optimization or final command output.

[0107] like Figure 2 The diagram shown is a functional block diagram of an energy uniform penetration system based on multi-physics field interference cancellation provided in an embodiment of the present invention.

[0108] The energy uniform penetration system based on multi-physics field interference destructive interaction described in this invention can be installed in electronic devices. Depending on the functions implemented, the energy uniform penetration system based on multi-physics field interference destructive interaction may include a biometric module, a pre-compensation module, a joint optimization module, a feedback acquisition module, a deviation correction module, and a convergence output module. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0109] In this embodiment, the functions of each module / unit are as follows: The biometric module is used to acquire biometric data of the target area and generate a biometric dataset of the target area based on the biometric data. The pre-compensation module is used to extract the initial energy parameters of the physical fields in the biometric dataset, and determine the interference destructive phase difference and amplitude compensation coefficient between the physical fields, so as to perform pre-compensation processing on the initial energy parameters and generate the pre-compensated energy parameters of the initial energy parameters. The joint optimization module is used to perform joint optimization processing on the pre-compensated energy parameters to generate a cooperative control command sequence for the pre-compensated energy parameters. The feedback acquisition module is used to respond to the coordinated control command sequence, acquire physiological feedback parameters in real time through a biofeedback sensor, and generate a real-time physiological feedback dataset of the physiological feedback parameters based on the physiological feedback parameters. The deviation correction module is used to compare the deviation between the real-time physiological feedback dataset and the preset target physiological response curve, and determine the frequency adjustment, intensity adjustment and duty cycle adjustment of the physical field according to the deviation value, and generate the updated energy parameters of the physical field. The convergence output module is used to compare the updated energy parameters with the preset energy uniformity convergence condition. If the preset energy uniformity convergence condition is not met, the updated energy parameters are used as the new pre-compensated energy parameters and returned to the joint optimization module. If the preset energy uniformity convergence condition is met, the current cooperative control instruction sequence is output as the final control instruction.

[0110] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0111] The modules described as separate components may or may not be physically separate. The components shown as modules 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.

[0112] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0113] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0114] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for uniform energy penetration based on multi-physics field interference destructive force, characterized in that, The method includes: S1. Obtain biometric data of the target area, and generate a biometric dataset of the target area based on the biometric data; S2. Extract the initial energy parameters of the physical fields in the biometric dataset, and determine the interference destructive phase difference and amplitude compensation coefficient between the physical fields to perform pre-compensation processing on the initial energy parameters and generate the pre-compensated energy parameters of the initial energy parameters. S3. Perform joint optimization processing on the pre-compensated energy parameters to generate a cooperative control command sequence for the pre-compensated energy parameters; S4. In response to the coordinated control command sequence, physiological feedback parameters are collected in real time through a biofeedback sensor, and a real-time physiological feedback dataset of the physiological feedback parameters is generated based on the physiological feedback parameters. S5. Compare the deviation between the real-time physiological feedback dataset and the preset target physiological response curve, and determine the frequency adjustment, intensity adjustment and duty cycle adjustment of the physical field based on the deviation value, and generate the updated energy parameters of the physical field. S6. Compare the updated energy parameters with the preset energy uniformity convergence condition. If the preset energy uniformity convergence condition is not met, the updated energy parameters are used as the new pre-compensated energy parameters, and the process returns to S3. If the preset energy uniformity convergence condition is met, the current cooperative control instruction sequence is output as the final control instruction.

2. The energy uniform penetration method based on multi-physics field interference destructive as described in claim 1, characterized in that, The process of acquiring biometric data of the target region and generating a biometric dataset of the target region based on the biometric data includes: Raw signal data from sensors is received through a biometric acquisition interface. The raw signal data includes tissue impedance signals, microcirculation blood flow signals, and local temperature signals. The original signal data is subjected to outlier removal and data smoothing to obtain the preprocessed feature signal of the original signal data. The preprocessed feature signal is segmented according to a preset time window, and the signal amplitude within the time window is normalized to obtain the normalized biosignal segment of the preprocessed feature signal. The normalized biomarker signal fragments are encapsulated into structured data fields of the target region according to signal type, and the structured data fields are combined into a biomarker dataset of the target region.

3. The energy uniform penetration method based on multi-physics field interference destructive as described in claim 1, characterized in that, The step of extracting the initial energy parameters of the physical fields in the biometric dataset and determining the destructive phase difference and amplitude compensation coefficient between the physical fields to pre-compensate the initial energy parameters and generate pre-compensated energy parameters includes: Physical field identification information is parsed from the biometric dataset, and the corresponding initial energy parameters are called according to the physical field identification information. The initial energy parameters include electromagnetic field frequency value, sound wave field phase value, far-infrared field intensity value, and photon radiation field duty cycle value. Based on the physical field identification information, read the interference cancellation phase difference and amplitude compensation coefficient between physical fields from the preset interference cancellation mapping relationship table; The phase value of the acoustic field is superimposed with the phase difference of the interference cancellation to obtain the adjusted phase value of the acoustic field. The electromagnetic field frequency value, the far-infrared field intensity value, and the photon radiation field duty cycle value are respectively subjected to amplitude weighting processing with the amplitude compensation coefficient to generate amplitude-adjusted parameter values ​​for the electromagnetic field frequency value, the far-infrared field intensity value, and the photon radiation field duty cycle value. The adjusted phase value and the adjusted amplitude parameter value are integrated to obtain the pre-compensated energy parameter of the initial energy parameter.

4. The energy uniform penetration method based on multi-physics field interference destructive as described in claim 1, characterized in that, The step of jointly optimizing the pre-compensated energy parameters to generate a coordinated control command sequence for the pre-compensated energy parameters includes: Extract the electromagnetic field frequency pre-compensation value, acoustic wave field phase pre-compensation value, far-infrared field intensity pre-compensation value, and photon radiation field duty cycle pre-compensation value from the pre-compensated energy parameters respectively. The electromagnetic field frequency pre-compensation value, the far-infrared field intensity pre-compensation value, and the photon radiation field duty cycle pre-compensation value are time-aligned to generate the time-aligned energy parameter group of the pre-compensated energy parameters. The phase pre-compensation value of the acoustic field and the time-aligned energy parameter group are subjected to phase-amplitude joint matching processing to generate a joint matching energy parameter set of the acoustic field phase pre-compensation value and the time-aligned energy parameter group; The joint matching energy parameter set is encoded and converted to obtain the coordinated control command sequence of the pre-compensated energy parameters.

5. The energy uniform penetration method based on multi-physics field interference destructive as described in claim 1, characterized in that, The process of responding to the coordinated control command sequence by acquiring physiological feedback parameters in real time through a biofeedback sensor and generating a real-time physiological feedback dataset based on the physiological feedback parameters includes: Receive the coordinated control command sequence and parse the acquisition start identifier and sensor channel configuration information in the coordinated control command sequence; Based on the acquisition start identifier and the sensor channel configuration information, the skin impedance sensor, microcirculation blood flow sensor and local temperature sensor in the biofeedback sensor group are activated to obtain the physiological feedback parameters of the sensors. The physiological feedback parameters are converted from analog to digital and marked with timestamps to obtain time-scaled physiological feedback parameters, which include changes in skin impedance, microcirculation blood flow acceleration, and temperature fluctuation amplitude. The time-stamped physiological feedback parameters are encapsulated according to the sampling channel and time order to obtain the real-time physiological feedback dataset of the time-stamped physiological feedback parameters.

6. The energy uniform penetration method based on multi-physics field interference destructive as described in claim 1, characterized in that, The step of comparing the real-time physiological feedback dataset with the preset target physiological response curve to determine the frequency adjustment, intensity adjustment, and duty cycle adjustment of the physical field based on the deviation value, and generating the updated energy parameters of the physical field, includes: Read the time-stamped physiological data from the real-time physiological feedback dataset, and simultaneously read the pre-stored target physiological response curve, which contains a sequence of expected values ​​with the same parameter type as the time-stamped physiological data; The skin impedance change, microcirculation blood flow acceleration, and temperature fluctuation amplitude in the time-scaled physiological data are compared with the corresponding expected values ​​in the expected value sequence to obtain the skin impedance deviation value, microcirculation blood flow deviation value, and temperature fluctuation deviation value of the time-scaled physiological data. The comprehensive deviation coefficient of the time-scaled physiological data is calculated based on the skin impedance deviation value, microcirculation blood flow deviation value, and temperature fluctuation deviation value. Based on the sign and magnitude of the comprehensive deviation coefficient, the corresponding frequency adjustment, intensity adjustment, and duty cycle adjustment are retrieved from the preset adjustment amount mapping table. The frequency adjustment, intensity adjustment, and duty cycle adjustment are respectively superimposed onto the corresponding terms of the current energy parameters of the physical field to obtain the updated energy parameters of the physical field.

7. The energy uniform penetration method based on multi-physics field interference destructive as described in claim 6, characterized in that, The formula for calculating the comprehensive deviation coefficient is as follows: ； In the formula, The comprehensive deviation coefficient is... This is the first weighting coefficient corresponding to the skin impedance deviation value. This is the second weighting coefficient corresponding to the microcirculation blood flow deviation value. This is the third weighting coefficient corresponding to the temperature fluctuation deviation value. This refers to the skin impedance deviation value. This refers to the microcirculation blood flow deviation value. This refers to the temperature fluctuation deviation value.

8. The energy uniform penetration method based on multi-physics field interference destructive as described in claim 1, characterized in that, The updated energy parameters are compared with the preset energy uniformity convergence condition. If the preset energy uniformity convergence condition is not met, the updated energy parameters are used as the new pre-compensated energy parameters, and the process returns to S3. If the preset energy uniformity convergence condition is met, the current cooperative control instruction sequence is output as the final control instruction, including: The updated values ​​of electromagnetic field frequency, acoustic wave field phase, far-infrared field intensity, and photon radiation field duty cycle are extracted from the updated energy parameters. The updated values ​​of electromagnetic field frequency, far-infrared field intensity, and photon radiation field duty cycle are compared one by one with the frequency threshold, intensity threshold, and duty cycle threshold in the preset energy uniformity convergence condition to obtain the frequency compliance indicator, intensity compliance indicator, and duty cycle compliance indicator of the updated energy parameters. The frequency compliance indicator, the intensity compliance indicator, and the duty cycle compliance indicator are compared with a preset compliance threshold. When all three indicators are in compliance status, it is determined that the preset energy uniformity convergence condition is met; otherwise, it is determined that the preset energy uniformity convergence condition is not met. In response to the determination result that the preset energy uniformity convergence condition is met, the current cooperative control command sequence is marked as the final control command, and the final control command is output. In response to the determination result that the preset energy uniformity convergence condition is not met, the updated energy parameter is used as the new pre-compensated energy parameter, and the process returns to S3.

9. The energy uniform penetration method based on multi-physics field interference destructive as described in claim 8, characterized in that, The step of comparing the frequency compliance indicator, the intensity compliance indicator, and the duty cycle compliance indicator with preset compliance thresholds includes: The frequency compliance indicator, the intensity compliance indicator, and the duty cycle compliance indicator are compared with preset compliance thresholds. When the frequency compliance indicator is not less than the preset compliance threshold, the frequency parameter is determined to be in compliance status; When the strength compliance indicator is not less than the preset compliance threshold, the strength parameter is determined to be in compliance status; When the duty cycle compliance indicator is not less than the preset compliance threshold, the duty cycle parameter is determined to be in compliance status. The results of collecting the frequency parameter, intensity parameter, and duty cycle parameter are used to generate a compliance feedback signal for joint optimization processing.

10. An energy uniform penetration system based on multi-physics field interference destructive force, characterized in that, The system for implementing the energy uniform penetration method based on multi-physics field interference destructive phase as described in claim 1 includes: The biometric module is used to acquire biometric data of the target area and generate a biometric dataset of the target area based on the biometric data. The pre-compensation module is used to extract the initial energy parameters of the physical fields in the biometric dataset, and determine the interference destructive phase difference and amplitude compensation coefficient between the physical fields, so as to perform pre-compensation processing on the initial energy parameters and generate the pre-compensated energy parameters of the initial energy parameters. The joint optimization module is used to perform joint optimization processing on the pre-compensated energy parameters to generate a coordinated control command sequence for the pre-compensated energy parameters; The feedback acquisition module is used to respond to the coordinated control command sequence, acquire physiological feedback parameters in real time through a biofeedback sensor, and generate a real-time physiological feedback dataset of the physiological feedback parameters based on the physiological feedback parameters. The deviation correction module is used to compare the deviation between the real-time physiological feedback dataset and the preset target physiological response curve, and determine the frequency adjustment, intensity adjustment and duty cycle adjustment of the physical field based on the deviation value, and generate the updated energy parameters of the physical field. The convergence output module is used to compare the updated energy parameters with the preset energy uniformity convergence condition. If the preset energy uniformity convergence condition is not met, the updated energy parameters are used as the new pre-compensated energy parameters and returned to the joint optimization module. If the preset energy uniformity convergence condition is met, the current cooperative control instruction sequence is output as the final control instruction.

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