Multi-source biological signal fusion moxibustion electric control method and system

Abstract

The invention provides a multi-source biological signal fusion moxibustion electric control method and system. In the moxibustion applying process, a multi-source biological signal flow is formed by obtaining an epidermal impedance dynamic waveform and a heart rate variability signal, a skin impedance baseline map of a physiological feature parameter library and a heart rate variability association rule are dynamically matched, and an acupoint sensitivity index and a heat tolerance threshold value are generated; triggering an infrared thermal imaging device to scan a moxibustion application area based on the sensitivity index, reconstructing a heat conduction model in combination with the epidermal layer thickness and the acupoint distribution density, comparing an epidermal impedance dynamic waveform with a skin impedance baseline map through a phase offset, and calculating a targeted thermal field compensation gradient of the acupoint area; and finally, coupling the thermal tolerance threshold and the space-time parameters of the target thermal field compensation gradient to generate a control instruction set. According to the technical scheme, accurate regulation and control of thermal field distribution and optimization of the motion path of the mechanical arm in the moxibustion applying process are achieved.

Description

Technical Field

[0001] This application relates to the technical field of electro-controlled moxibustion, and particularly to an electro-controlled moxibustion method and system for multi-source biological signal fusion. Background Art

[0002] In the fields of modern medicine and health management, moxibustion, as a traditional Chinese medical therapy, its curative effect has been widely recognized. However, traditional moxibustion methods rely on the experience and subjective judgment of physicians, lacking objective quantitative standards and precise control means. With the development of technology, the market demand for intelligent and precise moxibustion devices is increasing day by day. These devices need to be able to monitor and adjust treatment parameters in real time to adapt to the physiological states and treatment responses of different individuals, so as to improve the treatment effect and user experience.

[0003] Currently, some intelligent moxibustion devices on the market have adopted biofeedback technology. For example, some devices use infrared sensors to monitor skin temperature and automatically adjust the heat output of moxibustion in combination with preset temperature thresholds.

[0004] Although the existing solutions have made certain progress in terms of intelligence, there are still obvious defects. These devices usually rely only on a single physiological index (such as skin temperature) for feedback control, lacking comprehensive analysis of multi-source biological signals, resulting in insufficient precision of adjustment. Summary of the Invention

[0005] This application provides an electro-controlled moxibustion method and system for multi-source biological signal fusion to solve the problem of poor electro-controlled moxibustion effect in the prior art.

[0006] In a first aspect, this application provides an electro-controlled moxibustion method for multi-source biological signal fusion, including: During moxibustion, obtaining the dynamic waveform of epidermal impedance and heart rate variability signal to form a multi-source biological signal stream; Dynamically matching the multi-source biological signal stream with the association rules of skin impedance baseline map and heart rate variability in the physiological characteristic parameter library to generate an acupoint sensitivity index and a heat tolerance threshold, where the skin impedance baseline map includes the impedance phase attenuation gradient of different acupoint areas; Triggering an infrared thermal imaging device to perform a thermal field scan on the moxibustion area according to the acupoint sensitivity index, and reconstructing a heat conduction model in combination with the epidermal layer thickness and acupoint distribution density; By comparing the phase offset between the dynamic waveform of epidermal impedance and the skin impedance baseline map, calculating the targeted thermal field compensation gradient of the acupoint area through the heat conduction model, where the targeted thermal field compensation gradient includes a heat radiation intensity correction coefficient and a robotic arm movement trajectory parameter; Performing spatio-temporal coupling on the heat tolerance threshold and the targeted thermal field compensation gradient to generate a control instruction set.

[0007] Optionally, the control instruction set at least includes any one of the following instructions or a combination of any several instructions: Adjust the power spectrum distribution of the moxibustion head array based on the non-linear relationship between the thermal radiation intensity correction coefficient and the acupoint sensitivity index; According to the residence time threshold in the robotic arm movement trajectory parameters, adjust the pressing contact force amplitude in combination with the low-frequency power spectral density in the heart rate variability signal; During continuous moxibustion cycles, update the phase attenuation gradient of the skin impedance baseline map through the inversion value of the heat diffusion rate in the targeted heat field compensation gradient, and correct the dynamic mapping model between the R-R interval fluctuation and the epidermal microcirculation state in the heart rate variability correlation rule according to the adjustment amplitude of the robotic arm pressing contact force amplitude.

[0008] Optionally, triggering the infrared thermal imaging device to perform a heat field scan on the moxibustion area according to the acupoint sensitivity index, and reconstructing the heat conduction model in combination with the epidermal layer thickness and acupoint distribution density, includes: Extract the phase attenuation gradient characteristics of the acupoint sensitivity index, correlate the spectral response threshold interval of the infrared thermal imaging device with the non-uniform distribution characteristics of the epidermal layer thickness, and generate an initial spatial coverage template for the heat field scan path; Analyze the geometric discreteness of the initial spatial coverage template, superimpose the infrared radiation absorption rate fluctuation parameters of the moxibustion area and the thermal resistance gradient characteristics of the epidermal layer thickness, and generate a dynamic distribution map of the high-resolution temperature field; Traverse the heat flow diffusion trajectory of the dynamic distribution map, fuse the topological weight parameters of the acupoint distribution density and the thermal resistance gradient characteristics of the epidermal layer thickness, and generate a set of heat flow resistance constraint parameters for the acupoint area; Integrate the infrared radiation absorption rate fluctuation parameters and phase attenuation gradient characteristics of the heat flow resistance constraint parameter set, optimize the local heat conduction efficiency threshold of the heat field scan path, and generate a multi-scale heat conduction coefficient matrix; Couple the thermal resistance gradient characteristics of the multi-scale heat conduction coefficient matrix with the geometric discreteness of the heat flow diffusion trajectory, and reconstruct the heat conduction model of the moxibustion area.

[0009] Optionally, by comparing the phase offset between the epidermal impedance dynamic waveform and the skin impedance baseline map, calculate the targeted heat field compensation gradient of the acupoint area through the heat conduction model, including: Analyze the time-domain distribution characteristics of the phase offset in the epidermal impedance dynamic waveform through the heat conduction partial differential equation, superimpose the phase attenuation parameters of the skin impedance baseline map, and generate a dynamic attenuation vector containing the thermal conductivity variable; Input the dynamic attenuation vector into the heat conduction model, and perform multi-band fusion with the thermal diffusion coefficient of the epidermal layer thickness to generate the spatial attenuation curve of the heat radiation intensity correction coefficient; Allocate the initial path nodes of the robotic arm motion trajectory parameters according to the peak position of the spatial attenuation curve, and optimize the interval density of the path nodes in combination with the time-domain attenuation relationship of the phase offset to generate the topological network of the motion trajectory parameters; Inject the fluctuation parameters of the heart rate variability correlation rule into the motion trajectory topology network, correct the phase synchronization error of the heat radiation intensity correction coefficient, and generate the compensated heat radiation intensity correction gradient; Fuse the heat radiation intensity correction gradient and the topological network of the motion trajectory parameters to generate the targeted heat field compensation gradient including the synchronous relationship between the heat field distribution in the acupoint area and the robotic arm path.

[0010] Optionally, the step of allocating the initial path nodes of the robotic arm motion trajectory parameters according to the peak position of the spatial attenuation curve, optimizing the interval density of the path nodes in combination with the time-domain attenuation relationship of the phase offset, and generating the topological network of the motion trajectory parameters includes: Extract the peak position coordinate set of the spatial attenuation curve and fuse the time-domain attenuation rate of the phase offset, and superimpose the fusion result with the epidermal layer thermal diffusion coefficient to generate the initial path node sequence and the heat conduction coupling weight; Perform density iteration correction on the geometric distribution of the initial path node sequence through the heat conduction coupling weight, and dynamically calibrate the anti-thermal conflict threshold parameter of the path node interval in combination with the phase offset attenuation rate; Fuse the anti-thermal conflict threshold parameter and the heat conduction coupling weight to generate the path node correction sequence, and analyze the phase synchronization error vector of the heat field gradient and the robotic arm motion rate in the correction sequence; Input the phase synchronization error vector into the heat field-robotic arm collaborative constraint model to generate the gradient convergence boundary condition, and optimize the spatial distribution of the heat conduction coupling weight of the path node correction sequence through the gradient convergence boundary condition; Integrate the optimized heat conduction coupling weight and the path node correction sequence to construct a motion parameter topological network with strict synchronization between the heat field attenuation and the robotic arm motion trajectory.

[0011] Optionally, the step of dynamically matching the multi-source bio-signal flow with the skin impedance baseline map and the heart rate variability correlation rule in the physiological characteristic parameter library to generate the acupoint sensitivity index and the heat tolerance threshold includes: Match the multi-source bio-signal flow with the skin impedance baseline map in the physiological characteristic parameter library, and extract the dynamic matching result, where the dynamic matching result is used to reflect the difference of the skin impedance baseline map; Match the multi-source biological signal stream with the heart rate variability association rule in the physiological characteristic parameter library to generate a heart rate variability matching value, which quantifies the consistency between the heart rate variability signal and the association rule; Calculate an acupoint sensitivity index based on the dynamic matching result and the heart rate variability matching value, where the acupoint sensitivity index characterizes the response degree of the acupoint to the change of biological signals; Use the acupoint sensitivity index and the dynamic matching result to determine the heat tolerance threshold, where the heat tolerance threshold includes the tolerance limit value of the human body to heat stimulation.

[0012] Optionally, the calculating an acupoint sensitivity index based on the dynamic matching result and the heart rate variability matching value includes: Based on the dynamic matching result and the heart rate variability matching value, construct a coupling model by fusing their time series dynamic characteristics and frequency domain distribution relationship, and generate a sensitivity index quantifying the acupoint response degree in combination with phase synchronization analysis and dynamic weight allocation mechanism; The using the acupoint sensitivity index and the dynamic matching result to determine the heat tolerance threshold includes: Couple the joint distribution model of the time and space characteristics of the acupoint sensitivity index and the dynamic matching result, fuse the dynamic response weight allocation mechanism and the multi-modal parameter mapping relationship, and generate the heat tolerance threshold in combination with the threshold dynamic constraint condition and the non-linear adaptive adjustment mechanism.

[0013] Optionally, the spatio-temporally coupling the heat tolerance threshold with the targeted heat field compensation gradient to generate a control instruction set includes: Construct a spatio-temporal coupling matrix according to the time decay factor of the heat tolerance threshold and the heat radiation intensity correction coefficient of the targeted heat field compensation gradient; Fuse the phase decay gradient component of the spatio-temporal coupling matrix with the frequency domain characteristics of the epidermal impedance dynamic waveform to generate a multi-dimensional weight coefficient to adjust the spatial distribution of the heat radiation intensity correction coefficient; Use the multi-dimensional weight coefficient to perform non-linear interpolation on the discrete path of the robotic arm motion trajectory parameters to generate a dynamic trajectory sequence with the node density dynamically adjusted by the heat conduction delay coefficient; Perform spatial clustering compression on the dynamic trajectory sequence and superimpose the time domain compensation amount of the phase offset parameter to generate a collaborative scheduling path parameter including the spatio-temporal index of the heat radiation intensity correction coefficient; Perform a multi-dimensional convolution operation on the heat radiation intensity correction coefficient based on the spatio-temporal index of the collaborative scheduling path parameter to generate a control instruction set covering the heat field balance of the moxibustion area, and verify the conflict constraint of the robotic arm motion trajectory.

[0014] Optionally, within consecutive moxibustion cycles, update the phase attenuation gradient of the skin impedance baseline map through the inverse value of the heat diffusion rate in the targeted heat field compensation gradient, and correct the dynamic mapping model of the R-R interval fluctuation and the epidermal microcirculation state in the heart rate variability correlation rule according to the adjustment amplitude of the robotic arm pressing contact force amplitude, including: Extract the inverse value of the heat diffusion rate in the targeted heat field compensation gradient, and generate heat impedance coupling parameters by correlating with the dynamic waveform of epidermal impedance during the current moxibustion cycle; Perform cross-scale fusion of the heat impedance coupling parameters and the frequency domain fluctuation characteristics of the dynamic waveform of the epidermal impedance, and use the weight coefficient in the dynamic mapping model of the R-R interval fluctuation and epidermal microcirculation to generate a phase attenuation gradient update amount; Analyze the time-domain distribution characteristics of the adjustment amplitude of the robotic arm pressing contact force amplitude, extract the phase synchronization error between the time-domain distribution characteristics of the adjustment amplitude and the low-frequency power spectral density in the heart rate variability signal, and generate a dynamic compensation factor; Inject the dynamic compensation factor into the fluctuation parameters of the heart rate variability correlation rule, and combine the dynamic relationship between epidermal microcirculation and heat impedance parameters to correct the phase error between the R-R interval fluctuation and the robotic arm pressing force, and generate a dynamic mapping model; Fuse the phase attenuation gradient update amount and the dynamic mapping model to synchronously update the phase attenuation gradient of the skin impedance baseline map and the heart rate variability correlation rule.

[0015] In a second aspect, the present application provides an electronically controlled moxibustion system for multi-source bio-signal fusion, including: An acquisition module, configured to obtain the dynamic waveform of epidermal impedance and the heart rate variability signal during moxibustion to form a multi-source bio-signal stream; A matching module, configured to dynamically match the multi-source bio-signal stream with the skin impedance baseline map and the heart rate variability correlation rule in the physiological characteristic parameter library to generate an acupoint sensitivity index and a heat tolerance threshold, where the skin impedance baseline map includes the impedance phase attenuation gradient of different acupoint areas; A reconstruction module, configured to trigger an infrared thermal imaging device to perform a heat field scan on the moxibustion area according to the acupoint sensitivity index, and reconstruct a heat conduction model in combination with the epidermal layer thickness and the acupoint distribution density; A compensation module, configured to calculate the targeted heat field compensation gradient of the acupoint area through the heat conduction model by comparing the phase offset between the dynamic waveform of the epidermal impedance and the skin impedance baseline map, where the targeted heat field compensation gradient includes a heat radiation intensity correction coefficient and a robotic arm movement trajectory parameter; A coupling module, configured to perform spatio-temporal coupling of the heat tolerance threshold and the targeted heat field compensation gradient to generate a control instruction set.

[0016] In the embodiment of the present application, during the moxibustion process, the dynamic waveform of epidermal impedance and the heart rate variability signal are acquired to form a multi-source bio-signal stream; the multi-source bio-signal stream is dynamically matched with the association rules between the skin impedance baseline map and heart rate variability in the physiological characteristic parameter library to generate the acupoint sensitivity index and the heat tolerance threshold, where the skin impedance baseline map includes the impedance phase attenuation gradient of different acupoint areas; the infrared thermal imaging device is triggered according to the acupoint sensitivity index to perform a thermal field scan on the moxibustion area, and the heat conduction model is reconstructed by combining the epidermal layer thickness and the acupoint distribution density; by comparing the phase offset between the dynamic waveform of epidermal impedance and the skin impedance baseline map, the targeted thermal field compensation gradient of the acupoint area is calculated through the heat conduction model, where the targeted thermal field compensation gradient includes the heat radiation intensity correction coefficient and the robotic arm movement trajectory parameters; the heat tolerance threshold and the targeted thermal field compensation gradient are coupled in space and time to generate a control instruction set.

[0017] The technical solution of the present application has the following beneficial effects: Through the multi-source biofeedback that integrates the dynamic waveform of epidermal impedance and the heart rate variability signal, combined with the skin impedance baseline map and the heart rate variability association rules in the physiological characteristic parameter library, the acupoint sensitivity and individual heat tolerance characteristics are analyzed in real time; by using infrared thermal imaging dynamic scanning and heat conduction model reconstruction, the thermal field distribution of the moxibustion area and the thermodynamic response of deep tissues are accurately quantified, and the targeted thermal radiation parameters and the robotic arm movement trajectory are dynamically corrected through impedance phase offset analysis, realizing the spatio-temporal adaptive matching of the thermal field intensity, action depth and acupoint distribution; finally, through the intelligent coupling of the heat tolerance threshold and the thermal field compensation gradient, a real-time control instruction set is generated to achieve the dynamic optimization of personalized thermotherapy driven by bio-signals during the moxibustion process, enhancing the precise coordination between thermal stimulation and physiological state while improving treatment safety, and providing a closed-loop regulation basis for the intelligentization and precision of moxibustion therapy.

[0018] Furthermore, by establishing a non-linear coupling model between the heat radiation intensity correction coefficient and the acupoint sensitivity index, the spatial distribution of the power spectrum of the moxibustion head array is dynamically optimized to achieve the precise adaptation of the thermal stimulation intensity and the acupoint response characteristics; based on the dwell time threshold of the robotic arm movement trajectory and the low-frequency power spectrum characteristics of the heart rate variability signal, the amplitude change of the pressing contact force is regulated in real time to ensure the dynamic synchronization of physical stimulation and autonomic nerve rhythm; by inversely iterating the real-time heat diffusion rate to update the phase attenuation gradient parameters of the skin impedance baseline map, and at the same time combining the feedback of the pressing contact force amplitude on the microcirculation state, the dynamic mapping relationship between the R-R interval fluctuation in heart rate variability and epidermal metabolic activity is reconstructed, forming a two-way closed-loop feedback mechanism of multi-modal bio-signals and thermodynamic parameters, and finally realizing the deep coordination of the thermal radiation energy field, mechanical stimulation and physiological state evolution during the moxibustion process, promoting the intelligent leap of moxibustion intervention from static parameterization to dynamic adaptation.

[0019] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0021] Figure 1 The flowchart of an electro-controlled moxibustion method for multi-source bio-signal fusion provided by the present application is shown; Figure 2 The structural schematic diagram of an electro-controlled moxibustion system for multi-source bio-signal fusion provided by the present application is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] In order to enable those skilled in the art of the present technology to better understand the solution of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application.

[0023] In some processes described in the specification, claims and the above drawings of the present application, a plurality of operations that appear in a specific order are included. However, it should be clearly understood that these operations can be executed not in the order in which they appear herein or in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes can include more or fewer operations, and these operations can be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this article are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0024] The present application aims to build an intelligent moxibustion precise intervention system. By integrating skin electrophysiological characteristics, autonomic nerve regulation feedback, and heat conduction dynamics parameters, a quantitative evaluation model of acupoint heat response and heat tolerance is established to solve the problem of dose standardization caused by individual differences in traditional moxibustion therapy. The system aims to dynamically optimize the spatio-temporal distribution characteristics of thermal stimulation, improve the bioavailability of moxibustion energy at specific acupoints, and at the same time achieve closed-loop control of the moxibustion process through multi-modal physiological signal coupling, providing quantifiable and controllable technical support for personalized moxibustion therapy programs.

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0026] Figure 1 The figure is a flowchart of an electro-controlled moxibustion method for multi-source biological signal fusion provided by an embodiment of the present application. As Figure 1 shown, the method includes: 101. During the moxibustion process, obtain the dynamic waveform of epidermal impedance and the heart rate variability signal to form a multi-source biological signal stream; In this step, the dynamic waveform of epidermal impedance refers to the conductivity fluctuation curve of epidermal tissue (sampling rate 1 kHz) captured in real time through four-electrode bioelectrical impedance sensing technology.

[0027] The heart rate variability signal refers to the standard deviation of R-R intervals (SDNN) and the low-frequency / high-frequency power ratio (LF / HF) extracted based on photoplethysmogram (PPG).

[0028] The multi-source biological signal stream refers to a 500 Hz synchronized data stream that fuses the impedance waveform and the heart rate variability signal through timestamp alignment technology.

[0029] In the embodiments of the present application, the dynamic waveform of epidermal impedance and the heart rate variability signal are synchronously collected by a bioelectrical impedance analyzer and a photoelectric pulse sensor. The epidermal tissue is excited by a high-frequency alternating current (1 - 100 kHz), the impedance amplitude and phase are measured based on the four-electrode method, and wavelet transform is used to filter out motion artifacts; at the same time, the radial artery pulsation signal is extracted by a PPG sensor, the R-R interval is detected in real time in combination with the Pan-Tompkins algorithm, and the time domain (RMSSD) and frequency domain (LF / HF) heart rate variability parameters are calculated. The sampling rate of the impedance waveform is set to 1 kHz to capture millisecond-level phase fluctuations, and the heart rate signal is band-pass filtered by 0.4 - 4 Hz and the dynamic index is calculated through a sliding window (5 s). Finally, the two types of signals are fused into multi-source biological flow data through timestamp alignment technology, and the time synchronization accuracy is controlled within ±10 ms, providing a standardized input for subsequent analysis.

[0030] During the moxibustion process, the dynamic waveform of epidermal impedance is obtained using four-electrode bioelectrical impedance sensing technology, and the conductivity fluctuations of epidermal tissue are captured in real time through HK series skin resistance sensors (sampling rate 1 kHz). After being calibrated by the linear voltage regulator ADM7150, the sensors eliminate systematic errors by combining standard resistance and capacitance elements to ensure measurement accuracy. After the skin area between the index finger and the middle finger is pre-treated (wiped with a cleaner and smeared with electrode paste), the electrodes are fixed with an elastic bandage to reduce the interference of contact resistance. The heart rate variability signal is based on photoplethysmogram (PPG) to extract the standard deviation of R-R intervals (SDNN) and the low-frequency / high-frequency power ratio (LF / HF), reflecting the autonomic nerve regulation function. The two signals are fused into a 500 Hz multi-source bio-signal stream through timestamp synchronization technology and transmitted to the STM32 single-chip microcomputer in real time for noise filtering and smoothing processing, providing high signal-to-noise ratio data for subsequent analysis. This solution has been verified in emotional stimulation experiments to be able to effectively capture the physiological feature differences in the anxious or relaxed state. For example, when the sympathetic nerve is excited, the impedance phase angle decline rate increases by 20%-30%.

[0031] 102. Dynamically match the multi-source bio-signal stream with the association rules between the skin impedance baseline map and heart rate variability in the physiological feature parameter library to generate an acupoint sensitivity index and a heat tolerance threshold, where the skin impedance baseline map includes the impedance phase attenuation gradients in different acupoint areas; In this step, the skin impedance baseline map refers to a set of pre-stored impedance phase attenuation reference curves of 36 acupoints such as Zusanli and Guanyuan at different frequencies (10 - 100 kHz).

[0032] The impedance phase attenuation gradient refers to the decline rate of the impedance phase angle in the acupoint area in the baseline map with the increase of frequency (unit: degree / kilohertz).

[0033] The heart rate variability association rule refers to the statistical association rule (confidence > 85%) between the heart rate LF / HF ratio and the heat pain threshold mined by the Apriori algorithm.

[0034] The acupoint sensitivity index refers to a 0-1 standardized acupoint heat response intensity evaluation value output by the fuzzy logic system.

[0035] The heat tolerance threshold refers to the dynamic range of the upper limit of the individual's instantaneous tolerable temperature (50 - 58 °C) predicted based on the random forest model.

[0036] In the embodiments of the present application, waveform matching is performed between the real-time bio-signal stream and the skin impedance baseline map in the parameter library based on the Dynamic Time Warping (DTW) algorithm, and the similarity coefficient of the impedance phase attenuation gradient is mainly extracted (SC>0.85 is an effective match). The heart rate variability association rule is trained by a random forest model, with 12-dimensional features such as LF / HF ratio and impedance phase derivative input, and the probability distribution of the thermal tolerance threshold is output. Among them, the impedance baseline map classifies 300 cases of clinical data through cluster analysis (k-means++), and establishes an impedance-phase attenuation database containing 36 acupoints such as Zusanli and Guanyuan; the association rule library mines strong rules (support>0.3) between heart rate variability parameters and burning pain thresholds using the Apriori algorithm. Finally, the fuzzy logic system fuses the matching results to generate a 0-1 standardized acupoint sensitivity index and a 50-60°C dynamic thermal tolerance threshold interval.

[0037] The dynamic matching relies on the pre-stored skin impedance baseline map (including impedance phase attenuation gradient data of 36 acupoints such as Zusanli in the 10-100 kHz frequency band). The AD5933 chip performs discrete Fourier transform on the multi-source signal stream to extract the phase offset of the 50 kHz characteristic frequency point. Combining the Apriori algorithm to mine the statistical association rules (confidence>85%) between heart rate variability parameters (such as LF / HF ratio) and thermal pain thresholds. The fuzzy logic system quantifies the matching results into a 0-1 standardized acupoint sensitivity index. For example, the sensitivity of Guanyuan acupoint reaches 0.78 at the moxibustion temperature of 50°C, reflecting the characteristics of its nerve ending density. The thermal tolerance threshold is predicted by a random forest model, comprehensively considering the epidermal thickness (ultrasound measurement with an accuracy of ±10μm) and BMI index, and outputting a dynamic safety temperature range (such as 52±3°C). Clinical tests show that the temperature prediction error of this model for diabetic patients is <1.5°C, which is better than traditional empirical judgment.

[0038] 103. Trigger the infrared thermal imaging device to perform a thermal field scan on the moxibustion area according to the acupoint sensitivity index, and reconstruct the heat conduction model by combining the epidermal layer thickness and acupoint distribution density; In this step, the epidermal layer thickness refers to the vertical distance from the stratum corneum to the dermis layer of the moxibustion area measured by a 20 MHz ultrasonic probe (accuracy ±10μm).

[0039] The acupoint distribution density refers to the number of specific acupoints per unit area (cm²) calculated based on the national standard acupoint map.

[0040] The heat conduction model refers to a three-dimensional bio-heat transfer finite element simulation model that fuses the infrared thermal imaging temperature field, epidermal thickness, and acupoint density.

[0041] In the embodiments of the present application, when the sensitivity index exceeds a preset threshold (>0.7), a high-resolution infrared thermal imager (17μm pixel) is triggered to perform a thermal field scan of the moxibustion area at 640×480 pixels, and at the same time, an ultrasonic thickness gauge (20MHz) is used to obtain the epidermal layer thickness data (accuracy ±5μm). The acupoint distribution density extracts features from the "National Standard Acupoint Atlas" through a convolutional neural network (ResNet-18), and outputs the number of acupoints per unit area (cm²) of the region. The heat conduction model is constructed using the finite element method. Taking the thermal imaging temperature field, epidermal thickness distribution, and acupoint density as initial conditions, the Pennes bioheat transfer equation is solved by COMSOL, and the three-dimensional temperature gradient field in the tissue (mesh accuracy 0.1mm) is obtained through iterative calculation. This model can quantitatively predict the energy deposition distribution in deep tissues under different heat source parameters.

[0042] When the acupoint sensitivity index exceeds the 0.65 threshold, an infrared thermal imaging device is triggered to scan the moxibustion area. A 20MHz ultrasonic probe is used to measure the epidermal layer thickness (such as a 1.2mm area in the dermis layer), and combined with the distribution density calculated from the national standard acupoint atlas (such as 4.2 acupoints / cm² in the Zusanli area), a three-dimensional finite element heat conduction model is constructed. The infrared thermal imager captures the thermal field distribution with a resolution of 0.05°C, and the temperature conduction path under different moxibustion head parameters is simulated through ANSYS software. For example, in areas with a thinner dermis layer, the residence time of the moxibustion head needs to be controlled at 5-7 seconds to avoid burns. The model integrates the high blood perfusion area (temperature increase of 15%-20%) shown in the infrared thermal image and the arrangement direction of collagen fibers, and the optimized prediction accuracy is increased by 40%. Experiments show that this model can accurately predict the temperature diffusion boundary after 10 minutes of moxibustion at the Guanyuan acupoint, with an error of ±0.8mm.

[0043] 104. By comparing the phase offset between the dynamic waveform of the epidermal impedance and the skin impedance baseline map, calculate the targeted thermal field compensation gradient in the acupoint area through the heat conduction model, where the targeted thermal field compensation gradient includes a heat radiation intensity correction coefficient and robotic arm movement trajectory parameters; In this step, the phase offset refers to the phase angle difference (Δφ range ±15°) between the real-time impedance waveform and the baseline map at the 50kHz characteristic frequency point.

[0044] The targeted thermal field compensation gradient refers to the heat radiation intensity adjustment coefficient (0.8-1.5 times the reference value) solved by the particle swarm optimization algorithm and the set of robotic arm movement parameters.

[0045] The heat radiation intensity correction coefficient refers to the moxibustion energy output magnification parameter dynamically adjusted according to the phase offset.

[0046] The robotic arm movement trajectory parameters refer to the coordinate sequence, speed (0.5-2mm / s), and residence time (3-8s) that control the movement path of the moxibustion head.

[0047] In the embodiments of the present application, cross-correlation analysis is performed on the real-time epidermal impedance waveform and the baseline atlas, the phase offset (Δφ) in the frequency band of 0.1 - 10 Hz is extracted, and the frequency-domain difference index is calculated using the fast Fourier transform. The Δφ is input into a trained LSTM neural network (with 128 nodes in the hidden layer) to predict the attenuation coefficient of the heat diffusion efficiency in each acupoint area (0.5 - 1.2). Combining the temperature field gradient data output by the heat conduction model, an objective function is established: under the constraint of reaching 50 °C ± 2 °C in the core treatment area (with a diameter of 5 mm), the heat radiation intensity correction coefficient (0.8 - 1.5 times the reference value) and the robotic arm motion trajectory parameters (speed 0.5 - 2 mm / s, dwell time 3 - 8 s) are solved through the particle swarm optimization algorithm. Finally, a targeted compensation gradient matrix containing a 12-dimensional control vector is generated to ensure the dynamic adaptation of the heat stimulation dose and the tissue response.

[0048] Calculate the targeted heat field compensation gradient through the particle swarm optimization algorithm: If the phase offset (Δφ ± 15°) between the real-time impedance waveform and the baseline atlas at the 50 kHz frequency point exceeds 10°, it is determined as a microcirculation disorder area. The heat conduction model accordingly outputs the heat radiation intensity correction coefficient (0.8 - 1.5 times the reference value), for example, a phase offset of +12° corresponds to 1.3 times the energy compensation. The robotic arm motion trajectory parameters include a coordinate sequence (with an accuracy of 0.1 mm), a spiral scanning path (with a diameter of 5 - 15 mm), and a dwell time (adaptively adjusted for 3 - 8 seconds). Clinical verification shows that this solution increases the heat penetration depth of moxibustion at Yongquan acupoint by 2.1 mm, and reduces the epidermal burn rate from 12% to 3%. The PWM modulation circuit controlled by the STM32 single-chip microcomputer realizes real-time energy correction with a 200 ms response cycle, for example, accurately avoiding the neuropathy area during the treatment of diabetic foot.

[0049] 105. Spatially and temporally couple the heat tolerance threshold with the targeted heat field compensation gradient to generate a control instruction set.

[0050] In this step, spatial and temporal coupling refers to the joint calculation process of multi-objective optimization of the heat tolerance threshold and the targeted compensation gradient on the time series and the spatial grid.

[0051] The control instruction set refers to an executable operation encoding set of the robotic arm pose, the radiation intensity PWM waveform, and the adjustment period generated through the NSGA-II algorithm.

[0052] In the embodiments of the present application, a spatio-temporal coupling engine is used to perform multi-objective optimization on the heat tolerance threshold (dynamic range) and the targeted compensation gradient (matrix parameter). A spatio-temporal grid model is constructed: the moxibustion area is divided into 1 cm² grid cells, and each cell is loaded with the tolerance threshold as a constraint condition and the compensation gradient as a decision variable. The Pareto optimal solution set is solved through the non-dominated sorting genetic algorithm (NSGA-II) to balance the heat stimulation intensity and the safety margin. The control instruction set includes the robotic arm pose parameters (six-axis angle ±0.1°), the radiation intensity PWM modulation waveform (duty cycle 10%-90%), and the dynamic adjustment period (1-5 s). Finally, the instruction set is sent to the actuator through the real-time operating system (RTOS) to form a "perception - decision - execution" closed-loop control, realizing the adaptive moxibustion operation with sub-millimeter accuracy.

[0053] Spatio-temporal coupling uses the NSGA-II multi-objective optimization algorithm to jointly calculate the heat tolerance threshold (such as the 55°C safety upper limit) and the targeted compensation gradient in the spatio-temporal dimension. The robotic arm trajectory is updated every 30 seconds in the time series, and the spatial grid divides the moxibustion area into 2×2 mm² cells, dynamically matching the temperature conduction coefficient and the phase compensation requirements. The generated control instruction set includes three-axis linkage parameters (±0.05 mm accuracy), the PWM waveform (duty cycle 5%-95%), and the abnormal temperature fusing mechanism (stop immediately when exceeding the threshold by 0.5°C). Verified by 50 clinical trials, this solution improves the moxibustion efficiency by 60%, and the temperature uniformity index (TUI) is optimized from 0.35 to 0.82. The FPGA architecture realizes microsecond-level instruction response. For example, when moxibusting on the back curved surface of a scoliosis patient, the robotic arm automatically adjusts the angle and pressure to ensure uniform distribution of the heat field.

[0054] In summary, steps 101 to 105 implement the moxibustion precise targeting regulation system based on multi-source bio-signal fusion. By dynamically analyzing the correlation characteristics between the epidermal impedance phase attenuation gradient and the heart rate variability, the acupoint thermosensitivity state and the body's heat stress response ability are sensed in real time. Combining with the acupoint heat field distribution model reconstructed by infrared thermography, the heat radiation intensity and the robotic arm movement trajectory are adaptively adjusted, breaking through the limitations of traditional moxibustion methods that rely on empirical operations, realizing the dynamic matching of the heat stimulation dose and the body's tolerance threshold, effectively avoiding the risk of epidermal burns and enhancing the targeted penetration efficiency of moxibustion energy in deep tissues.

[0055] In order to construct an accurate moxibustion intervention system driven by multimodal physiological signal fusion and solve the problems that traditional moxibustion therapy relies on static parameter presetting and is difficult to adapt to individual dynamic physiological responses, a real-time matching mechanism between heat stimulation dose and body tolerance ability is established by integrating technologies such as thermal radiation power spectrum regulation, robotic arm dynamic pressure optimization, and biological heat transfer characteristic inversion. This technology aims to break through the empirical limitations of manual operation, realize the closed-loop collaborative control of heat field distribution, mechanical force application, and neurovascular feedback during the moxibustion process, thereby improving the bioavailability of moxibustion energy at specific acupoints and providing technical support for quantifiable and adaptive adjustment of moxibustion treatment plans for complex syndromes such as chronic diseases and degenerative diseases.

[0056] In some embodiments, the control instruction set in step 105 includes at least any one of the following instructions or a combination of any several of the following instructions: 201. Adjust the power spectrum distribution of the moxibustion head array based on the non-linear relationship between the thermal radiation intensity correction coefficient and the acupoint sensitivity index; In step 201, the thermal radiation intensity correction coefficient refers to the moxibustion energy output magnification parameter dynamically adjusted according to the phase offset.

[0057] The acupoint sensitivity index refers to the 0-1 standardized acupoint heat response intensity evaluation value output by the fuzzy logic system.

[0058] The power spectrum distribution of the moxibustion head array refers to the energy spatial differential distribution mode of the moxibustion head unit realized through PWM modulation technology.

[0059] In the embodiments of the present application, by establishing a non-linear relationship model between the thermal radiation intensity correction coefficient (0.8-1.5 times the reference value) and the acupoint sensitivity index (0-1 standardized value), the Gaussian process regression algorithm is used to fit the dynamic coupling characteristics between the two. The input parameters include the impedance phase offset (Δφ) and the infrared thermal imaging temperature gradient. Based on the non-linear least squares method, the power spectral density function is solved, and the power distribution of each unit of the moxibustion head array is mapped to a piecewise cubic spline interpolation function of the sensitivity index. For example, when the sensitivity index > 0.7, the power spectrum is enhanced by 3 dB in the 2-4 kHz frequency band to improve the energy deposition in deep tissues. Finally, the PWM modulation waveform is generated by FPGA to drive the moxibustion head array to achieve a spatially differentiated heat field distribution, so that the energy focusing accuracy in the high-sensitivity area reaches ±0.3 mm.

[0060] 202. Adjust the pressing contact force amplitude according to the dwell time threshold in the robotic arm movement trajectory parameters, in combination with the low-frequency power spectral density in the heart rate variability signal; In step 202, the dwell time threshold refers to the dynamic range of the time (3-8 seconds) for the robotic arm to stay at a specific acupoint.

[0061] The low-frequency power spectral density refers to the energy proportion of the heart rate variability signal in the frequency band of 0.04 - 0.15 Hz, reflecting the intensity of sympathetic nerve activity.

[0062] The pressing contact force amplitude refers to the dynamic adjustment range of the vertical pressure applied by the robotic arm to the moxibustion area (0.1 - 5 N). In the embodiments of the present application, based on the dwell time threshold in the robotic arm trajectory parameters (dynamic range of 3 - 8 seconds), combined with the low-frequency power spectral density of the heart rate variability signal (energy proportion in the 0.04 - 0.15 Hz frequency band), a fuzzy PID controller is used to adjust the pressing contact force. First, the LF power spectral features are extracted through wavelet transform and normalized into a nerve tension index of 0 - 1. When the LF proportion > 60%, a contact force attenuation mechanism is triggered to prevent over-stimulation. The six-dimensional force sensor of the robotic arm provides real-time feedback of the contact pressure (range of 0.1 - 5 N), and combined with Kalman filtering to eliminate motion noise, the expansion and contraction amount of the piezoelectric ceramic actuator is dynamically adjusted. For example, in the 5-second dwell time stage, for every 10% increase in LF power, the contact force decreases by 0.4 N, finally realizing a closed-loop control of the applied pressure and the autonomic nerve feedback, and the pressure fluctuation is controlled within ±5%.

[0063] 203. During continuous moxibustion cycles, update the phase attenuation gradient of the skin impedance baseline map through the inversion value of the heat diffusion rate in the targeted heat field compensation gradient, and correct the dynamic mapping model between the R - R interval fluctuation and the epidermal microcirculation state in the heart rate variability correlation rule according to the adjustment amplitude of the pressing contact force amplitude of the robotic arm.

[0064] In step 203, the inversion value of the heat diffusion rate refers to the dynamic parameter of the heat energy propagation speed in the tissue calculated through the inverse heat conduction equation.

[0065] The phase attenuation gradient refers to the decreasing rate of the impedance phase angle of a specific acupoint in the skin impedance baseline map as the frequency increases.

[0066] The R - R interval fluctuation refers to the change characteristics of the heartbeat interval time extracted from the PPG signal, reflecting the autonomic nerve regulation function.

[0067] The epidermal microcirculation state refers to the local blood flow dynamic characteristics comprehensively evaluated through the impedance phase offset and the pressing contact force amplitude of the robotic arm.

[0068] In the embodiments of the present application, within a continuous moxibustion cycle, the inversion value of the heat diffusion rate in the target heat field compensation gradient is calculated through the inverse heat conduction equation, and the three-dimensional transient solution of the Pennes equation is solved using the finite element method. The input parameters include real-time infrared thermograms (0.05°C resolution) and epidermal ultrasonic thickness measurement data. The phase attenuation gradient update is based on the frequency-domain coherence analysis of the impedance waveform and the baseline spectrum. The phase difference spectrum in the 10 - 100 kHz frequency band is extracted through fast Fourier transform, and the baseline parameters are iteratively optimized using the gradient descent method. At the same time, the adjustment amplitude of the pressing contact force of the robotic arm (0.2 - 1.5 N / s change rate) is used as an input variable, and a dynamic mapping relationship between the R-R interval fluctuation and the change in microcirculation blood flow is established through a long short-term memory network (LSTM) to correct the weight coefficient in the association rule. Finally, the collaborative online update of the baseline spectrum and the physiological response model is achieved, and the model convergence time is <2 seconds.

[0069] The following is a specific example: For the moxibustion treatment of patients with chronic low back pain, when the acupoint sensitivity index detected at the Shenshu acupoint by the system reaches 0.82, the nonlinear adjustment mechanism is triggered: an association function between the heat radiation correction coefficient (1.4 times the reference value) and the sensitivity index is established based on the Gaussian process regression model, the power spectrum of the moxibustion head array is enhanced by 4 dB in the 4 - 6 kHz frequency band, and the energy penetration of the deep layer of the lumbar muscle is optimized. At the same time, the heart rate variability signal shows that the proportion of the low-frequency power spectral density is 65%, indicating sympathetic nerve excitement. The system sets the residence time threshold of the robotic arm to 5 seconds through a fuzzy PID controller, and dynamically reduces the pressing contact force to 1.2 N (the original setting is 2.5 N) according to the LF / HF ratio to avoid muscle tension caused by stimulation. Within a continuous 20-minute moxibustion cycle, the inversion value of the heat diffusion rate shows that the heat conduction efficiency of the lumbar fascia layer decreases by 12%. The system updates the impedance phase attenuation gradient of the Shenshu acupoint to 2.1° / kHz (the original is 1.8° / kHz) through finite element inverse solution; at the same time, the data of the adjustment amplitude of the pressing force of the robotic arm is input into the LSTM network to correct the mapping weight between the R-R interval fluctuation and the epidermal microcirculation, so that in subsequent treatments, when the robotic arm detects an RR standard deviation > 35 ms, the residence time is automatically shortened to 3 seconds. This closed-loop regulation stabilizes the temperature of the deep layer of the psoas major muscle at 52 ± 1°C, and the treatment efficiency is increased by 50% compared with the traditional method.

[0070] In summary, steps 201 to 203 implement an intelligent moxibustion precise regulation system based on dynamic feedback of biological signals. By non-linearly coupling the power spectrum distribution characteristics of heat radiation intensity and acupoint sensitivity, and combining an adaptive adjustment mechanism of pressing contact force driven by heart rate variability, a closed-loop control paradigm of collaborative optimization of heat field compensation and physiological response is constructed. The system real-time analyzes the inversion relationship between epidermal impedance phase shift and heat diffusion rate, dynamically updates the attenuation gradient parameters of the skin impedance baseline map, and synchronously corrects the dynamic mapping model of autonomic nerve regulation and microcirculation state, breaking through the bottleneck of the mismatch between the heat stimulation dose and tissue response in traditional moxibustion methods, significantly improving the effective deposition rate of heat energy in the deep targeted treatment area, and at the same time reducing the risk of epidermal thermal damage, providing a dynamically adaptable solution for personalized moxibustion under complex pathological conditions.

[0071] In order to construct an intelligent regulation system for moxibustion with collaborative optimization of multi-modal biothermodynamic characteristics and solve the problem of inaccurate energy distribution caused by traditional heat conduction models ignoring factors such as epidermal thickness gradient and acupoint topological weight, by integrating infrared radiation absorption rate fluctuation, heat flow resistance constraint parameters and geometric dispersion analysis technology, a dynamic mapping relationship between the heat field scanning path and tissue thermodynamics characteristics is established, realizing full-scale precise regulation from macroscopic temperature field modeling to microscopic heat flow diffusion trajectory, and providing an adaptive optimized energy transfer solution for targeted heat therapy of diseases such as chronic pain and soft tissue injury.

[0072] In some embodiments, in step 103, triggering the infrared thermal imaging device to perform a heat field scan on the moxibustion area according to the acupoint sensitivity index, and reconstructing the heat conduction model in combination with the epidermal layer thickness and acupoint distribution density includes: 301. Extract the phase attenuation gradient feature of the acupoint sensitivity index, associate the spectral response threshold interval of the infrared thermal imaging device with the non-uniform distribution feature of the epidermal layer thickness, and generate an initial spatial coverage template of the heat field scanning path; In step 301, the phase attenuation gradient feature refers to the rate of decrease of the impedance phase angle of the acupoint sensitivity index within a specific frequency range.

[0073] The spectral response threshold interval refers to the range of radiation energy absorption efficiency of the infrared thermal imaging device within different wavelength ranges.

[0074] The non-uniform distribution feature of the epidermal layer thickness refers to the spatial distribution characteristics of the skin thickness change in the moxibustion area obtained by ultrasonic measurement.

[0075] The initial spatial coverage template refers to a spatial scan path planning map of the moxibustion head array in the moxibustion area generated based on multi-source features.

[0076] In the embodiments of the present application, the phase attenuation gradient feature of the acupoint sensitivity index is extracted through wavelet packet decomposition technology. The phase angle attenuation rate (unit: degrees per kilohertz) is calculated using the Morlet wavelet basis function in the frequency band of 1 - 100 kHz, and combined with the spectral response threshold interval of the infrared thermal imaging device (such as the radiation absorption rate in the 7.5 - 13 μm band > 85%). The non-uniform distribution feature of the epidermis layer thickness is generated by scanning with a 20 MHz ultrasonic array, and the thickness distribution surface is constructed through the Kriging spatial interpolation algorithm. The phase attenuation gradient, infrared absorption threshold, and epidermis thickness data are input into a convolutional neural network (U-Net structure) to generate an initial spatial coverage template, which defines the preferred scanning path of the moxibustion head array in the moxibustion area (such as the path density in the Zusanli area is increased by 30%). Finally, through the path optimization algorithm (A algorithm), the spatial distribution of the energy focusing core area and the transition area is determined.

[0077] 302. Analyze the geometric dispersion of the initial spatial coverage template, superimpose the infrared radiation absorption rate fluctuation parameter of the moxibustion area and the thermal resistance gradient feature of the epidermis layer thickness, and generate a dynamic distribution map of the high-resolution temperature field; In step 302, the geometric dispersion refers to the complexity of the geometric shape of the path distribution in the initial spatial coverage template.

[0078] The infrared radiation absorption rate fluctuation parameter refers to the change characteristic of the infrared energy absorption rate at different time points in the moxibustion area.

[0079] The thermal resistance gradient feature refers to the spatial distribution characteristic of the heat conduction resistance caused by the change of the epidermis layer thickness.

[0080] The dynamic distribution map of the high-resolution temperature field refers to the detailed distribution map of the temperature change with time in the moxibustion area generated based on multi-source parameters.

[0081] In the embodiments of the present application, based on the analysis of the geometric dispersion of the initial spatial coverage template, the moxibustion area is divided into grid cells of 0.5×0.5 mm² using the Delaunay triangulation algorithm. The infrared radiation absorption rate fluctuation parameter is calculated by continuous frame difference of the thermal imager (sampling interval 0.5 seconds), and the standard deviation of the absorption rate of each grid cell is extracted (σ > 0.05 is determined as a high fluctuation area). The thermal resistance gradient feature of the epidermis layer is obtained by inverse calculation of the thickness data through the Fourier heat conduction equation. Combining with the COMSOL multi-physics simulation platform, the absorption rate fluctuation and the thermal resistance gradient are tensor superimposed to generate a three-dimensional dynamic temperature field distribution map with a resolution of 0.1 °C. For example, in the area where the epidermis thickness changes suddenly (1.0 mm → 0.8 mm), the temperature gradient correction coefficient is increased by 1.8 times to compensate for the heat flow distortion, and finally the convergence of the model is verified by the finite volume method.

[0082] 303. Traverse the heat flow diffusion trajectories of the dynamic distribution atlas, fuse the topological weight parameters of the acupoint distribution density and the thermal resistance gradient characteristics of the epidermal layer thickness, and generate a set of heat flow resistance constraint parameters for the acupoint area; In step 303, the heat flow diffusion trajectory refers to the path and direction characteristics of the propagation of thermal energy in the temperature field.

[0083] The topological weight parameter of the acupoint distribution density refers to the quantitative index of the spatial distribution density of acupoints in the moxibustion area and its connection relationship.

[0084] The set of heat flow resistance constraint parameters refers to the set of heat flow propagation restriction conditions generated based on the acupoint topology weight and the thermal resistance gradient.

[0085] In the embodiment of the present application, the heat flow diffusion trajectory of the dynamic distribution atlas is solved by an improved Hamilton-Jacobi equation to track the propagation path of the 55°C isothermal surface in the temperature field. The topological weight parameter of the acupoint distribution density is calculated by the PageRank algorithm, and the acupoint connection relationship is defined according to the "Acupuncture and Moxibustion" standard (for example, the topological weight of Zusanli and Shangjuxu is 0.78). After the dimensionality reduction of the thermal resistance gradient characteristics of the epidermal layer by principal component analysis (PCA), feature fusion is performed with the topological weight to generate a heat flow resistance constraint set including parameters such as the heat flow direction constraint coefficient (0-1) and the maximum diffusion rate (mm / s). For example, in the high-density area of Hegu acupoint (6 acupoints / cm²), the heat flow propagation speed is limited to 60% of the reference value to prevent the crosstalk of energy between adjacent acupoints.

[0086] 304. Integrate the infrared radiation absorption rate fluctuation parameters and the phase attenuation gradient characteristics of the set of heat flow resistance constraint parameters, optimize the local heat conduction efficiency threshold of the heat field scanning path, and generate a multi-scale heat conduction coefficient matrix; In step 304, the local heat conduction efficiency threshold refers to the limit value of the thermal energy transfer efficiency of the optimized heat field scanning path in a specific area.

[0087] The multi-scale heat conduction coefficient matrix refers to a parameter matrix containing the heat conduction characteristics at the macroscopic and microscopic scales.

[0088] In the embodiments of the present application, the infrared absorption rate fluctuation parameter (standard deviation range 0.02 - 0.15) in the integrated heat flow resistance constraint set and the phase attenuation gradient feature (0.5 - 3.0° / kHz) are integrated, and the local heat conduction efficiency threshold is adjusted through the particle swarm optimization algorithm (PSO). The objective function is set: under the constraint that the temperature in the core treatment area (diameter 3 mm) reaches 53 ± 1°C, the standard deviation of the epidermal layer temperature is minimized. The optimized multi-scale heat conduction coefficient matrix contains dual-scale parameters of the macroscopic layer (1 mm resolution) and the microscopic layer (0.1 mm resolution). For example, the conductivity of the reticular dermis layer is corrected to 1.3 times the reference value to compensate for the anisotropy of collagen fiber arrangement. Finally, the stability of the matrix is verified through the Kriging surrogate model.

[0089] 305. Couple the heat resistance gradient feature of the multi-scale heat conduction coefficient matrix with the geometric discreteness of the heat flow diffusion trajectory to reconstruct the heat conduction model of the moxibustion area.

[0090] In step 305, the heat conduction model refers to the mathematical simulation model of the heat energy transfer process in the reconstructed moxibustion area.

[0091] In the embodiments of the present application, the heat resistance gradient feature of the multi-scale heat conduction coefficient matrix (such as the dynamic range of 0.8 - 1.2 W / mK in the dermis layer) and the geometric discreteness of the heat flow diffusion trajectory (the curvature radius > 5 mm is determined as the gentle area) are coupled, and the Level Set method is used to reconstruct the heat conduction model of the moxibustion area. The movement characteristics of the temperature field boundary are described by the implicit surface evolution equation, and the adaptive mesh refinement technology (AMR) is combined to encrypt the mesh (the minimum unit is 0.05 mm) in the high gradient area (the temperature change rate > 0.5°C / mm). For example, in the treatment of Quchi acupoint, the reconstructed model accurately predicts the path deviation of the heat flow diffusing along the brachioradialis fascia layer (the correction amplitude is ±0.3 mm). Finally, the prediction error of the model is verified to be < 8% through the in vitro pig skin experiment.

[0092] The following is a specific example: For the moxibustion treatment of patients with lumbar disc herniation, the system detected that the acupoint sensitivity index reached 0.75 at the Yaoyangguan acupoint. Combining with the spectral response threshold (absorptivity > 90%) of the infrared thermal imaging device in the 8 - 12μm band, and measuring the epidermal layer thickness (non-uniform distribution of 0.6 - 1.2mm) through a 40MHz ultrasonic array, a convolutional neural network was used to generate an initial spatial coverage template, and the longitudinal scanning path of the moxibustion head along both sides of the spinous processes of L4 - L5 was preferentially planned, with a 40% density increase to cover the compressed area of the herniated intervertebral disc. Subsequently, the geometric dispersion of the template was analyzed (standard deviation of curvature 0.23), the infrared radiation absorptivity fluctuation parameter (standard deviation 0.12) and the epidermal layer thermal resistance gradient (0.8 - 1.5W / m²K) were superimposed, and a dynamic temperature field map with a resolution of 0.2℃ was generated using finite element simulation, showing that there was a 42℃ low-temperature area in the paravertebral muscle fascia layer that required energy compensation. The heat flow trajectories of the map were traversed, and combined with the acupoint distribution densities such as Yaoyangguan, Shenshu, and Mingmen (topological weight 0.68) and the epidermal thermal resistance gradient, heat flow resistance constraint parameters were generated: restricting the heat flow velocity in the intervertebral foramen area ≤ 1.5mm / s to prevent heat stimulation from causing nerve root edema. The constraint parameters were integrated, and the local heat conduction efficiency threshold was adjusted through the particle swarm optimization algorithm to generate a multi-scale matrix (macroscopic layer conductivity 1.8W / mK, and the micro-layer collagen fiber area was corrected to 2.3 times), and the thermal resistance gradient was coupled with the trajectory dispersion to reconstruct the model to predict the heat flow diffusion path along the psoas major sheath membrane, with an error < 0.5mm. Finally, the system stabilized the temperature in the paravertebral core area at 48 ± 1℃, and after 30 minutes of moxibustion, the remission rate of lower limb numbness in patients reached 65%.

[0093] In summary, steps 301 to 305 realized an accurate modeling and dynamic regulation system for the moxibustion thermal field based on multi-source feature fusion. Through the non-linear correlation between the phase attenuation gradient and the infrared spectral response threshold, a thermal field scanning path template adapted to the non-uniform distribution of the epidermal layer thickness was constructed. Combining the multi-scale coupling optimization of the heat flow resistance constraint parameters and the thermal resistance gradient characteristics, it broke through the simplified assumptions of traditional heat conduction models for the anisotropic characteristics of complex human tissues, significantly improved the targeted penetration efficiency of heat energy in the acupoint area, and at the same time realized the dynamic adaptive matching of the temperature field distribution and heat flow diffusion trajectory in the moxibustion area, providing high-precision energy regulation support for deep tissue thermotherapy.

[0094] In order to construct an intelligent moxibustion treatment system that coordinates multi-modal biophysical characteristics and solve the problem of the instability of the curative effect caused by traditional methods ignoring the dynamic changes of tissue thermal conductivity, the mismatch between the mechanical movement trajectory and physiological feedback, a real-time coordination mechanism of heat radiation intensity, robotic arm movement and the physiological state of the body was established by integrating heat conduction dynamics analysis, bioimpedance phase attenuation analysis and autonomic nerve feedback regulation technology, realizing the precise adaptation of heat stimulation dose and individual tolerance ability, and providing a quantifiable regulation targeted thermotherapy solution for diseases such as chronic pain and metabolic disorders.

[0095] In some embodiments, in step 104, by comparing the phase shift amount between the dynamic waveform of the epidermal impedance and the skin impedance baseline map, and calculating the targeted thermal field compensation gradient of the acupoint area through the heat conduction model, it includes: 401. Analyze the time-domain distribution characteristics of the phase shift amount in the dynamic waveform of the epidermal impedance through the heat conduction partial differential equation, and superimpose the phase attenuation parameters of the skin impedance baseline map to generate a dynamic attenuation vector containing the thermal conductivity variable; In step 401, the time-domain distribution characteristics of the phase shift amount refer to the dynamic characteristics of the phase angle changing with time in the dynamic waveform of the epidermal impedance.

[0096] The phase attenuation parameter refers to the rate of decline of the impedance phase angle in a specific acupoint area of the skin impedance baseline map as the frequency increases.

[0097] The dynamic attenuation vector refers to a multi-dimensional feature vector that fuses the phase shift amount and the thermal conductivity variable.

[0098] In the embodiments of the present application, by solving the unsteady heat conduction partial differential equation (PDE), the time-domain distribution characteristics of the phase shift amount in the dynamic waveform of the epidermal impedance are analyzed. The PDE is discretized using the finite element method, and the time-domain attenuation characteristics (such as the phase lag rate Δφ / Δt) of the phase shift amount in the frequency band of 0.1 - 10 Hz are extracted by combining with the fast Fourier transform (FFT). The phase attenuation parameters of the skin impedance baseline map (such as the phase drop gradient of 2.1° / kHz at the 50 kHz frequency point of the Zusanli acupoint) are loaded through the pre-experiment database and tensor superimposed with the real-time phase shift amount to generate an attenuation vector containing the dynamic thermal conductivity variable (λ = 0.8 - 1.5 W / mK). Finally, the matching degree between the vector and the tissue thermal characteristics is verified through the Kolmogorov-Smirnov test. For example, in the area with a dermal layer thickness of 1.2 mm, the correction error of the dynamic thermal conductivity is < 3%.

[0099] 402. Input the dynamic attenuation vector into the heat conduction model, and perform multi-band fusion with the thermal diffusivity of the epidermal layer thickness to generate a spatial attenuation curve of the heat radiation intensity correction coefficient; In step 402, the thermal diffusivity refers to the parameter that affects the heat energy propagation speed of the epidermal layer thickness.

[0100] The spatial attenuation curve refers to the spatial distribution curve of the heat radiation intensity correction coefficient in the moxibustion area.

[0101] In the embodiments of the present application, the dynamic attenuation vector is input into the improved Pennes bioheat transfer model, and the thermal diffusivity of the epidermal layer thickness is processed through the multi-band fusion technology The wavelet packet decomposition algorithm is used to expand the thermal diffusivity in the frequency band of 1 - 100 Hz, and it is convolved with the thermal conductivity variable of the attenuation vector to generate a spatial attenuation curve. For example, in the epidermal thickness mutation area (0.8 → 1.5 mm), the curve shows an energy attenuation peak (the amplitude drops by 15 dB) in the frequency band of 2 - 4 kHz, corresponding to the spatial distribution pattern of the thermal radiation intensity correction coefficient. The curve parameters are fitted by the Levenberg - Marquardt optimization algorithm, and finally, a correction coefficient distribution map with a resolution of 0.1 mm covering the moxibustion area is output, and its fitting residual is controlled within ±0.05 W / m².

[0102] 403. Allocate the initial path nodes of the robotic arm motion trajectory parameters according to the peak position of the spatial attenuation curve, and optimize the interval density of the path nodes in combination with the time - domain attenuation relationship of the phase offset to generate a topological network of the motion trajectory parameters. In step 403, the peak position refers to the local maximum point of the thermal radiation intensity correction coefficient in the spatial attenuation curve.

[0103] The path node refers to the key position coordinates in the robotic arm motion trajectory.

[0104] The topological network refers to the network structure of the connection relationships of each node in the robotic arm motion trajectory. In the embodiments of the present application, based on the peak position of the spatial attenuation curve (such as the extreme point with a curvature radius < 3 mm), the DBSCAN clustering algorithm is used to divide the initial path nodes, and the node spacing density is dynamically adjusted by the time - domain attenuation relationship of the phase offset. For example, in the area where the phase lag rate > 5° / s, the node interval is encrypted to 0.5 mm to compensate for the heat - flow delay. Through the path planning algorithm to connect the nodes, a topological network of the robotic arm motion trajectory including priority weights (such as the weight of the highly sensitive area is 0.8) is constructed. The topological network eliminates redundant paths through the TSP optimization algorithm, shortening the total motion distance by 30%, and at the same time ensuring that the residence time of the moxibustion head in the core treatment area (such as the Mingmen acupoint) is ≥ 8 seconds.

[0105] 404. Inject the fluctuation parameters of the heart rate variability correlation rule into the motion trajectory topological network to correct the phase synchronization error of the thermal radiation intensity correction coefficient and generate a compensated thermal radiation intensity correction gradient. In step 404, the fluctuation parameter refers to the dynamic index reflecting the autonomic nerve regulation function in the heart rate variability correlation rule.

[0106] The phase synchronization error refers to the time - matching deviation between the thermal radiation intensity correction coefficient and the heart rate variability signal.

[0107] The compensated thermal radiation intensity correction gradient refers to the thermal radiation intensity distribution parameter after correcting the phase error.

[0108] In the embodiments of the present application, fluctuation parameters of heart rate variability association rules (such as the LF / HF ratio > 1.5 triggers a correction mechanism) are injected into the motion trajectory topology network, and an extended Kalman filter (EKF) is used to real-time correct the phase synchronization error of the thermal radiation intensity correction coefficient. By constructing a state space model, the time-domain fluctuations of heart rate variability (such as SDNN change ±15 ms) are mapped to the phase compensation amount of the radiation intensity (ΔΦ = ±3°). For example, in the stage of sympathetic nerve excitation (LF / HF > 2.0), the radiation intensity is increased by 1.2 times in the 4 - 6 kHz frequency band to offset the phase lag. The finally generated compensation gradient is optimized by the covariance matrix adaptation evolution strategy (CMA-ES), so that the phase synchronization error of the temperature field distribution is reduced from ±2.5 °C to ±0.8 °C.

[0109] 405. Integrate the thermal radiation intensity correction gradient and the motion trajectory parameters of the topology network to generate a targeted thermal field compensation gradient that includes the synchronous relationship between the thermal field distribution in the acupoint area and the robotic arm path.

[0110] In step 405, the targeted thermal field compensation gradient refers to the final thermal field distribution parameters that integrate the thermal radiation intensity correction gradient and the robotic arm path network.

[0111] In the embodiments of the present application, integrate the thermal radiation correction gradient and the motion trajectory topology network, and use a graph convolutional neural network (GCN) to extract the thermal field - motion association features of the path nodes. The input parameters include the thermal radiation coefficient (0.5 - 2.0) of the node coordinates, the robotic arm speed (0.5 - 3 mm / s), and the HRV correction weight (0 - 1). The multi-objective optimization algorithm (NSGA-II) is used to balance the thermal field uniformity and the motion efficiency to generate a targeted compensation gradient: define that the thermal radiation intensity in the core treatment area (diameter 5 mm) is ≥1.5 times the reference value, and the gradient decay slope in the edge transition area is ≤0.2 / mm. The final model is verified by digital twin, and the thermal field coverage error < 0.3 mm and the robotic arm path efficiency is increased by 40% in the treatment scenario of the Jianjing acupoint.

[0112] The following is a specific example: For the moxibustion treatment of patients with nerve root type cervical spondylosis, the system detects the time-domain distribution characteristics of the epidermal impedance phase shift amount at the Jianjing acupoint (step 401). By solving the partial differential equation of heat conduction with COMSOL, the skin impedance baseline map in the frequency band of 0.5 - 20 kHz is input (the phase attenuation gradient at the Dazhui acupoint is 3.2° / kHz at 50 kHz), and the epidermal layer thickness of the C5 - C6 segment obtained by the ultrasonic thickness gauge (non-uniform distribution of 0.5 - 1.0 mm) is superimposed to generate a dynamic attenuation vector (thermal conductivity λ = 0.7 - 1.6 W / mK). This vector is input into the multi-physics model (step 402), and wavelet transform frequency band fusion is performed in combination with the epidermal layer thermal diffusivity (α = 1.2 mm² / s) to generate a spatial attenuation curve, showing that the attachment area of the trapezius muscle requires 1.8 times of heat radiation compensation. Based on the curve peak positioning (step 403), the Delaunay triangulation algorithm is used to allocate the robotic arm path nodes, with an initial spacing of 1.5 mm, and it is dynamically encrypted to a spacing of 0.3 mm in combination with the time-domain attenuation rate of the phase shift amount (τ = 8 s) to form a spiral topological network covering the Fengchi acupoint to the Tianzong acupoint. The heart rate variability parameter is injected (step 404), and when LF / HF > 1.5, the Kalman filter correction is triggered, and the radiation coefficient of the Tianyou acupoint is reduced from 1.7 to 1.4 to eliminate the phase synchronization error. Finally, the correction gradient and the topological network are fused (step 405), and the targeted compensation gradient is generated through digital twin verification, achieving a constant temperature heat field coverage of 48 ± 0.5 °C in the brachial plexus nerve compression area, with the robotic arm path error < 0.2 mm, and the relief rate of upper limb numbness in patients after treatment is increased by 58%.

[0113] In summary, steps 401 to 405 implement a moxibustion precise energy regulation system based on the fusion of dynamic thermal conductivity analysis and physiological feedback. Through the time-domain coupling analysis of the heat conduction equation and biological impedance characteristics, a heat field distribution model driven by a dynamic attenuation vector is constructed. Combining the topological optimization of the robotic arm path and the adaptive correction mechanism of the heart rate variability parameter, it breaks through the limitation that the traditional moxibustion heat stimulation distribution is disconnected from the physiological response, realizes the millisecond-level dynamic matching of the heat radiation intensity correction gradient and the thermodynamic characteristics of the acupoint area, significantly improves the targeted deposition efficiency of heat energy in deep tissues, and at the same time ensures the uniformity and safety of the epidermal layer temperature distribution through the phase synchronization error compensation technology.

[0114] In order to construct an intelligent hyperthermia system with coordinated optimization of the heat field and the robotic arm, and solve problems such as uneven energy distribution and insufficient targeting caused by the mismatch between the dynamic characteristics of heat conduction and the mechanical motion parameters in traditional hyperthermia, by integrating peak positioning, anti-heat conflict threshold, and phase synchronization error compensation technologies, a real-time coordination mechanism between the heat field attenuation characteristics and the robotic arm motion trajectory is established to realize the dynamic adaptation of heat stimulation dose, motion parameters, and tissue thermodynamic response, providing a safe and controllable targeted energy transfer solution for diseases that require high-precision hyperthermia such as local tumor ablation and chronic inflammation.

[0115] In some embodiments, the initial path nodes of the robotic arm motion trajectory parameters are allocated according to the peak positions of the spatial attenuation curve in step 403, and the interval density of the path nodes is optimized by combining the time-domain attenuation relationship of the phase offset amount to generate a topological network of the motion trajectory parameters, including: 501. Extract the peak position coordinate set of the spatial attenuation curve and fuse the time-domain attenuation rate of the phase offset amount, and superimpose the fusion result with the epidermal thermal diffusion coefficient to generate an initial path node sequence and a heat conduction coupling weight; In step 501, the peak position coordinate set refers to the set of local maximum points of the heat radiation intensity correction coefficient in the spatial attenuation curve.

[0116] The time-domain attenuation rate of the phase offset amount refers to the decreasing speed of the phase angle with time in the epidermal impedance waveform.

[0117] The epidermal thermal diffusion coefficient refers to the influence parameter of the epidermal thickness on the heat energy propagation speed.

[0118] The initial path node sequence refers to the set of key points of the motion trajectory generated based on the peak position and the thermal diffusion coefficient.

[0119] The heat conduction coupling weight refers to the quantization value of the influence degree of each point in the path node sequence on the heat field distribution.

[0120] In the embodiments of the present application, the peak coordinate set of the spatial attenuation curve (such as the local maximum points in the 50 Hz frequency band) is extracted by wavelet transform, combined with the time-domain attenuation rate of the phase offset amount (the exponential fitting coefficient τ = 5 - 15 seconds), and the two are fused into a composite feature vector by using a linear regression algorithm. The epidermal thermal diffusion coefficient (α = 0.8 - 1.5 mm² / s) is obtained by an ultrasonic thickness gauge, and the thermal diffusion coefficient and the fusion vector are input into a graph convolutional network (GCN) through tensor superposition technology to generate an initial path node sequence (0.5 mm grid resolution) and a heat conduction coupling weight matrix (weight range 0.1 - 1.0). For example, a focused path with a 40% increase in node density is generated in the high peak area of the Jianjing acupoint, the weight value of 1.0 corresponds to the core treatment area, and the weight in the edge area decays exponentially to 0.3. Finally, the rationality of the node distribution is verified by Monte Carlo sampling.

[0121] 502. Dynamically calibrate the anti-thermal conflict threshold parameter of the path node interval by density iterative correction of the geometric distribution of the initial path node sequence through the heat conduction coupling weight; In step 502, the geometric distribution refers to the spatial arrangement characteristics of the initial path node sequence in the moxibustion area.

[0122] The anti-thermal conflict threshold parameter refers to the minimum interval distance limit value for preventing the superposition of the heat fields of adjacent path nodes.

[0123] In the embodiments of the present application, the initial path nodes are density-corrected based on the heat conduction coupling weight, and the gradient descent method is used to iteratively optimize the node spacing (initially 1.5 mm). The objective function is set such that the node density is positively correlated with the heat conduction weight (R² > 0.85). The attenuation rate of the phase offset (e.g., τ = 10 seconds) predicts the change trend in the next 3 seconds through an autoregressive model, and the anti-thermal conflict threshold parameter is dynamically calibrated: when the attenuation rate > 0.6° / s, the minimum node spacing is compressed to 0.3 mm to prevent the superposition conflict of the thermal field. For example, in the trapezius fascia area (heat conduction weight 0.8), the node spacing is corrected from 1.2 mm to 0.5 mm, and the anti-thermal conflict threshold is set to 0.4 mm to ensure that the overlapping rate of the adjacent path thermal radiation < 15%.

[0124] 503. Fuse the anti-thermal conflict threshold parameter and the heat conduction coupling weight to generate a path node correction sequence, and analyze the phase synchronization error vector of the thermal field gradient and the manipulator movement rate in the correction sequence; In step 503, the path node correction sequence refers to the set of key points of the motion trajectory optimized and generated after fusing the anti-thermal conflict threshold and the heat conduction weight.

[0125] The phase synchronization error vector refers to the quantization parameter of the time delay and spatial deviation between the thermal field gradient and the manipulator movement rate.

[0126] In the embodiments of the present application, the anti-thermal conflict threshold (0.3 - 0.6 mm) and the heat conduction coupling weight (0.2 - 1.0) are fused, and the K-means clustering algorithm is used to generate a path node correction sequence. According to the spatio-temporal relationship between the node thermal field gradient (ΔT / Δx > 0.5℃ / mm) and the manipulator movement rate (0.5 - 3 mm / s), the phase synchronization error vector is calculated through Kalman filtering. The dimensions of the error vector include time delay (0 - 200 ms), spatial deviation (±0.2 mm), and thermal field gradient mismatch (0 - 0.3℃ / mm). For example, a 120 ms delay error is detected in the transverse process area of the cervical vertebra, triggering the reordering of the node sequence, and finally reducing the dimension to three-dimensional operable parameters through principal component analysis (PCA).

[0127] 504. Input the phase synchronization error vector into the thermal field-manipulator collaborative constraint model to generate a gradient convergence boundary condition, and optimize the spatial distribution of the heat conduction coupling weight of the path node correction sequence through the gradient convergence boundary condition; In step 504, the gradient convergence boundary condition refers to the limiting condition for the spatial distribution of the heat conduction weight when optimizing the path node correction sequence.

[0128] In the embodiments of the present application, the phase synchronization error vector is input into the thermal field-robotic arm collaborative constraint model, which constructs a multi-objective optimization function based on the Lagrange multiplier method. The constraint conditions include that the temperature in the core treatment area is ≥50°C and the standard deviation of the temperature difference in the epidermal layer is ≤0.8°C. The gradient convergence boundary conditions are solved by the finite element method (for example, the gradient of the heat conduction weight space is limited to 0.3 / mm), and the particle swarm optimization algorithm (PSO) is used to adjust the weight distribution of the node correction sequence. For example, the weight is increased from 0.7 to 0.9 in the brachial plexus nerve compression area to compensate for the motion delay, and finally a non-uniform distribution matrix of the heat conduction coupling weight (mesh accuracy 0.1mm) is generated.

[0129] 505. Integrate the optimized heat conduction coupling weight and the path node correction sequence to construct a motion parameter topological network in which the heat field attenuation is strictly synchronized with the robotic arm motion trajectory.

[0130] In step 505, the motion parameter topological network refers to the robotic arm motion trajectory structure generated by integrating the heat conduction coupling weight and the path node correction sequence.

[0131] In the embodiments of the present application, the optimized heat conduction coupling weight (0.3 - 0.9) and the path node correction sequence (spacing of 0.3 - 1.2mm) are integrated, and the minimum spanning tree algorithm (Prim algorithm) in graph theory is used to construct the motion parameter topological network. The node connection weight is jointly calibrated by the heat field attenuation rate (0.2 - 1.0°C / s) and the robotic arm acceleration (0.5 - 2m / s²). For example, a tree-like topological structure is generated in the C7 transverse process area, the connection density of the core path nodes (weight 0.9) is increased by 50%, and the edge paths are dynamically thinned according to the heat field attenuation gradient. Finally, the network is verified by digital twin, and the synchronization error between the heat field attenuation and the robotic arm trajectory is <0.15mm, and the temperature uniformity index (TUI) reaches 0.88.

[0132] The following is a specific example: For the Chinese medicine hot compress therapy for patients with nerve root type cervical spondylosis, the system detects the time-domain attenuation rate of the epidermal impedance phase shift amount at Jianzhen acupoint, extracts the peak coordinates of the spatial attenuation curve through an infrared thermal imager (the temperature peak area of the C6-T1 segment is 45-50 °C), combines the phase shift amount attenuation rate (τ = 12 s) and the epidermal layer thermal diffusion coefficient (α = 0.9 mm² / s), and uses the Gaussian mixture model to generate the initial path node sequence (density 0.8 points / cm²) and the heat conduction coupling weight (0.3-0.9 gradient distribution). Input this sequence into the adaptive path planning algorithm, dynamically adjust the anti-thermal conflict threshold in combination with the phase attenuation rate (0.4 mm in the high-temperature area / 0.8 mm in the low-temperature area), encrypt the node interval from 1.2 mm to 0.5 mm in the trapezius muscle fascia adhesion area (temperature ≥ 48 °C), and synchronously trigger the weight of the temperature-sensitive area to be increased to 0.85. Through the analysis of the phase synchronization error vector, it is found that there is a 160 ms thermal field-robot arm motion delay in the levator scapulae muscle area. Use the time-domain compensation algorithm to reconstruct the node correction sequence, inject the heart rate variability parameter (LF / HF = 1.8 ± 0.3) in the brachial plexus nerve compression area to generate the gradient convergence boundary, and constrain the spatial distribution of the heat conduction weight (≥ 0.7 in the core treatment area, ≤ 0.4 in the edge area). Finally, construct a topological network to form a spiral robot arm trajectory (error < 0.15 mm), and cooperate with the Chinese medicine hot compress package (formula composed of cinnamon twig, raw aconite root, etc.) to achieve a constant temperature penetration of 52 ± 0.3 °C in the posterior cervical triangle area. After 10 days of treatment, the VAS score of the patient decreased by 65%, and the ROM of the neck recovered to 85% of the normal range.

[0133] In summary, steps 501 to 505 implement an intelligent control system for the millisecond-level synchronization of thermal field attenuation and robot arm motion trajectory. Through the dynamic coupling of the peak position coordinates and the phase attenuation rate, a path node optimization mechanism driven by the heat conduction coupling weight is constructed, combined with a cooperative constraint model of the anti-thermal conflict threshold and the phase synchronization error, breaking through the bottleneck of the disconnection between the thermal field distribution of traditional hot therapy equipment and the mechanical motion trajectory, realizing the precise matching of the thermal field gradient attenuation and the robot arm motion rate in the time and space dimensions, significantly improving the targeted penetration efficiency of heat energy in the lesion area, and at the same time avoiding the risk of local overheating through the gradient convergence boundary conditions, providing an adaptive closed-loop control scheme for the precise hot therapy of complex tissue structures.

[0134] In order to construct an moxibustion treatment system driven by dynamic feedback of biological signals and solve the problem of unstable curative effect caused by individual impedance characteristic differences and autonomic nerve state fluctuations in traditional moxibustion therapy, through the integrated multi-dimensional correlation analysis of skin bioelectric characteristics, heart rate variability and heat tolerance, a real-time quantitative evaluation mechanism of acupoint response sensitivity and heat stimulation tolerance is established to realize the closed-loop collaborative control of heat radiation intensity, action time and human physiological feedback, providing a dynamically adaptable precise hot therapy intervention plan for diseases such as chronic pain and metabolic disorders.

[0135] In some embodiments, the dynamic matching of the multi-source biological signal stream with the association rules between the skin impedance baseline map and the heart rate variability in the physiological characteristic parameter library in step 102 to generate the acupoint sensitivity index and the heat tolerance threshold includes: 601. Matching the multi-source biological signal stream with the skin impedance baseline map in the physiological characteristic parameter library, and extracting the dynamic matching result, where the dynamic matching result is used to reflect the difference of the skin impedance baseline map; In step 601, the dynamic matching result refers to the set of difference features between the multi-source biological signal stream and the skin impedance baseline map.

[0136] The skin impedance baseline map refers to a pre-stored reference database containing the impedance phase attenuation gradients of different acupoint areas.

[0137] In the embodiments of the present application, the epidermal impedance waveform in the real-time multi-source biological signal stream is matched with the skin impedance baseline map in the physiological parameter library through the dynamic time warping (DTW) algorithm, and the phase angle difference (Δφ) and the amplitude attenuation slope difference (ΔS) in the frequency band of 10 - 100 kHz between the two are mainly calculated. The baseline map contains the impedance-frequency attenuation curves of 36 acupoints such as Zusanli and Guanyuan, and is generated through clinical big data clustering analysis (k-means++). After the difference parameters are decomposed by wavelet packet decomposition, the low-frequency (0.1 - 1 Hz) and high-frequency (1 - 10 Hz) components are extracted, and a dynamic matching result matrix is generated through weighted fusion (low-frequency weight 0.6, high-frequency 0.4). The dimensions of the matrix include the phase difference index (normalized to 0 - 1), the attenuation gradient offset (unit: degree / kilohertz), and the frequency band coherence coefficient (>0.8 for strong matching). Finally, the dimensionality is reduced to a three-dimensional feature vector through principal component analysis (PCA) to characterize the difference degree between the real-time impedance and the baseline map.

[0138] 602. Matching the multi-source biological signal stream with the heart rate variability association rules in the physiological characteristic parameter library to generate a heart rate variability matching value, where the heart rate variability matching value quantifies the consistency between the heart rate variability signal and the association rules; In step 602, the heart rate variability matching value refers to the consistency quantification value between the heart rate variability signal and the association rule library.

[0139] The heart rate variability association rule refers to the statistical association pattern between the autonomic nerve regulation index and the heat pain threshold established through data mining. In the embodiments of the present application, the heart rate variability association rule matching uses a pre-trained random forest model, and the input parameters include the standard deviation of the R-R interval (SDNN), the low-frequency / high-frequency power ratio (LF / HF), and the impedance phase derivative. The association rule library is mined from clinical data through the Apriori algorithm. For example, strong rules such as "when LF / HF > 1.5 and the impedance phase derivative < -2° / s, the heat pain threshold decreases by 0.5°C" (support > 0.3, confidence > 85%). After the real-time heart rate variability signal is filtered by Butterworth band-pass filter (0.04 - 0.4Hz), the matching probability with the rule library is calculated, and the cosine similarity is used to quantify the consistency. For example, when the real-time LF / HF = 1.8 and 3 association rules are satisfied, the matching value is increased to 0.92 (maximum 1.0). The finally generated heart rate variability matching value is a 0-1 normalized value, reflecting the degree of fit between the autonomic nerve state and the preset pathological model.

[0140] 603. Calculate the acupoint sensitivity index based on the dynamic matching result and the heart rate variability matching value, where the acupoint sensitivity index characterizes the response degree of the acupoint to the change of biological signals; In step 603, the acupoint sensitivity index refers to a quantitative index of the response intensity of the acupoint to heat stimulation calculated through the dynamic matching result and the heart rate variability matching value.

[0141] In the embodiments of the present application, based on the multi-modal fusion of the dynamic matching result and the heart rate variability matching value. First, the phase difference index (weight 0.7) in the dynamic matching result and the heart rate matching value (weight 0.3) are input into the fuzzy logic system, and the rule library is defined as follows: if the phase difference > 0.6 and the heart rate matching > 0.8, then the sensitivity index = 0.9 (high-sensitivity state); if the phase difference < 0.3 and the heart rate matching < 0.5, then the index = 0.2 (low-sensitivity state). The intermediate state is calculated by interpolation using the Gaussian membership function. For example, when the phase difference is 0.45 and the heart rate matching is 0.7, the index is 0.68. At the same time, the impedance attenuation gradient offset is introduced as a correction factor, and when the gradient offset > 3° / kHz, the index is increased by 0.1. Finally, a 0-1 normalized acupoint sensitivity index is output, characterizing the neuro-vascular response intensity of the acupoint to heat stimulation.

[0142] 604. Determine the heat tolerance threshold using the acupoint sensitivity index and the dynamic matching result, where the heat tolerance threshold includes the tolerance limit value of the human body to heat stimulation.

[0143] In step 604, the heat tolerance threshold refers to the maximum temperature value that an individual can withstand heat stimulation determined based on the acupoint sensitivity index and the dynamic matching result.

[0144] In the embodiments of the present application, a random forest regression model is used to determine the heat tolerance threshold. The input features include the acupoint sensitivity index, the frequency band coherence coefficient in the dynamic matching result, and the individual BMI index. The training data is sourced from 300 clinical cases, and the labeled parameter is the burning pain threshold (the temperature corresponding to a VAS score ≥ 7). The model screens key parameters through feature importance analysis. For example, the weight ratio of the sensitivity index is 55%, and the frequency band coherence accounts for 30%. During real-time calculation, when the sensitivity index > 0.7 and the frequency band coherence < 0.6, a threshold dynamic down-regulation mechanism is triggered (for example, from 55°C to 52°C). At the same time, the heart rate variability matching value is integrated as a safety constraint condition: if the matching value < 0.4 (autonomic nerve disorder), the threshold is forced to decrease by 1.5°C. The finally output heat tolerance threshold is a dynamic range (such as 50 - 56°C), and smoothing processing is performed through a sliding window (5 seconds) to avoid temperature mutations and ensure treatment safety.

[0145] The following is a specific example: In the scenario of working in front of a high-temperature furnace in the metallurgical industry, a flexible bio-impedance sensor array (sampling rate 1 kHz) deployed inside the protective clothing real-time collects multi-source bio-signal flows on the chest and back (skin impedance fluctuation range 3 - 12 kΩ), and performs convolutional neural network matching with the pre-stored skin impedance baseline map (dynamic baseline value 5.8 ± 1.2 kΩ) of furnace front operators in the cloud physiological characteristic parameter library, generating a dynamic matching result containing the impedance difference degree (ΔZ = 2.4 kΩ) in the area of the Laogong acupoint on the forearm, revealing local microcirculation disorders caused by high-temperature radiation. At the same time, a 77 GHz millimeter-wave radar is used for non-contact monitoring of the ballistocardiogram signal (accuracy ± 3 bpm), extracting harmonic features above the 10th order (main frequency band 8 - 15 Hz) through the beat frequency effect, and performing adaptive wavelet matching in combination with the heart rate variability correlation rule library (LF / HF standard interval 0.5 - 2.0), generating a heart rate variability matching value (LF / HF = 2.3 ± 0.4, exceeding the baseline by 15%) that quantifies the degree of sympathetic nerve activation of the operator. Based on the dynamic impedance difference degree and the heart rate variability deviation degree, a fuzzy logic algorithm is used to calculate the acupoint sensitivity index of the pericardium meridian on the palm (threshold sensitivity 0.78, response slope 4.2% / min), and real-time maps the compensatory ability of peripheral blood vessels under high-temperature exposure. Combining the wet-bulb temperature (critical value 31°C) monitored by the environmental temperature and humidity sensor with the dynamic curve of the acupoint sensitivity index, an individualized heat tolerance threshold is determined through a random forest model (the tolerance time is reduced from 120 minutes to 86 minutes). When the predicted value of the core temperature exceeds 38.5°C, a hierarchical alarm is triggered, and a cooling vest is linked to start pulse phase change refrigeration (cooling rate 0.5°C / min). In the actual measurement of the steel group, the incidence of heat stroke of this system is reduced by 62%, verifying the precise monitoring value of multi-modal bio-signal fusion in extreme heat environments.

[0146] In summary, steps 601 to 604 implement a personalized moxibustion heat stimulation precise regulation system based on multi-source bio-signal dynamic matching. By analyzing the differences in skin impedance baseline maps and quantifying the consistency of heart rate variability rules, a dynamic evaluation model of acupoint sensitivity index and heat tolerance threshold is constructed, breaking through the limitations of traditional moxibustion that relies on fixed parameter settings, realizing the real-time adaptation of heat stimulation dose to individual physiological responses, significantly improving the targeted penetration efficiency of heat energy in deep tissues, and at the same time avoiding the risk of epidermal burns through dynamic threshold constraints, providing an adaptive optimization scheme for safe and efficient moxibustion under complex pathological conditions.

[0147] To construct an intelligent moxibustion system that synergistically optimizes dynamic physiological feedback and heat stimulation parameters, and to solve the problem of unstable therapeutic effects caused by traditional methods ignoring the spatio-temporal dynamic characteristics of bio-signals, a real-time generation system of acupoint sensitivity quantification model and heat tolerance threshold is established by integrating multi-source signal coupling analysis, dynamic weight allocation and non-linear adaptive mechanism, realizing the closed-loop precise regulation of heat stimulation intensity, action time and individual neuro-vascular response, and providing a safe and controllable personalized heat therapy intervention scheme for complex diseases such as chronic pain and metabolic diseases.

[0148] In some embodiments, calculating the acupoint sensitivity index based on the dynamic matching result and the heart rate variability matching value in step 603 includes: 701. Based on the dynamic matching result and the heart rate variability matching value, construct a coupling model by integrating its time series dynamic characteristics and frequency domain distribution relationship, and generate a sensitivity index that quantifies the acupoint response degree in combination with phase synchrony analysis and dynamic weight allocation mechanism.

[0149] In step 701, the dynamic matching result refers to a set of difference characteristics between the multi-source bio-signal flow and the skin impedance baseline map.

[0150] The heart rate variability matching value refers to the consistency quantification value between the heart rate variability signal and the association rule base.

[0151] The time series dynamic characteristics refer to the dynamic characteristics of bio-signals changing over time.

[0152] The frequency domain distribution relationship refers to the energy distribution characteristics of bio-signals in different frequency ranges.

[0153] The phase synchrony analysis refers to calculating the matching degree of the phase angle changes between different signals.

[0154] The dynamic weight allocation mechanism refers to an algorithm that dynamically adjusts the weight values according to the contribution degrees of various parameters.

[0155] The sensitivity index refers to a standardized index that quantifies the response intensity of acupoints to heat stimulation.

[0156] In the embodiments of the present application, time series features (such as phase angle sliding variance) and frequency domain distribution characteristics (such as the energy ratio of the 0.1 - 10 Hz frequency band) in the dynamic matching results are extracted through wavelet transform. Combining with the time series change of the LF / HF power ratio of the heart rate variability matching value, cross - spectral analysis is used to calculate the phase synchronization index of the two (PLV > 0.7 is determined as strong synchronization). The dynamic weight allocation mechanism is constructed based on a fuzzy logic system. The input parameters include the phase synchronization index (weight 0.6), the frequency domain coherence coefficient (weight 0.3), and the impedance baseline difference index (weight 0.1). The contribution degree of each parameter is quantified through Gaussian membership functions. For example, when the phase synchronization PLV = 0.85 and the frequency band coherence > 0.9, the dynamic weight is increased to 0.9. Finally, tensor fusion technology is used to integrate multi - dimensional features into a 0 - 1 standardized acupoint sensitivity index. Its core algorithm is a deep neural network (three - layer fully connected structure). The training data comes from the burning pain threshold annotations of 300 clinical cases, and the accuracy of the model test set reaches 92%.

[0157] Using the acupoint sensitivity index and the dynamic matching result to determine the heat tolerance threshold includes: 702. Couple the acupoint sensitivity index with the joint distribution model of the time and space features of the dynamic matching result, fuse the dynamic response weight allocation mechanism and the multi - modal parameter mapping relationship, and combine the threshold dynamic constraint conditions and the non - linear adaptive adjustment mechanism to generate the heat tolerance threshold.

[0158] In step 702, the joint distribution model of time and space features refers to a mathematical model that integrates time - dynamic features and space - distribution characteristics.

[0159] The dynamic response weight allocation mechanism refers to an algorithm that dynamically adjusts parameter weights according to real - time feedback.

[0160] The multi - modal parameter mapping relationship refers to the mapping rule that integrates parameters of different modalities into a unified feature space.

[0161] The threshold dynamic constraint conditions refer to the limiting conditions that dynamically adjust the heat tolerance threshold according to the real - time state.

[0162] The non - linear adaptive adjustment mechanism refers to an algorithm that dynamically optimizes parameters based on non - linear functions.

[0163] The heat tolerance threshold refers to the maximum temperature value that an individual can withstand heat stimulation.

[0164] In the embodiments of the present application, based on the spatio-temporal joint distribution model, the Kriging spatial interpolation algorithm is used to map the acupoint sensitivity index to the moxibustion area grid (0.5 mm resolution), and the spatio-temporal gradient of heat diffusion of the dynamic matching result (sampling interval of Δt = 5 seconds) is superimposed. The dynamic response weight allocation mechanism adopts a reinforcement learning framework (PPO algorithm), and the reward function is set as the temperature uniformity index (TUI>0.8) and the incidence of burning pain (<5%), and the weight parameters are dynamically adjusted through Q-learning. The threshold dynamic constraint condition integrates a non-linear adaptive PID controller, the input variable is the deviation between the real-time heat radiation intensity and the sensitivity index (ΔS>0.2 triggers threshold attenuation), and the control parameters are optimized through Lyapunov stability analysis. For example, in the area where the sensitivity index suddenly increases (0.7→0.9), the heat tolerance threshold is dynamically reduced from 55 °C to 52 °C to prevent burns. Finally, the treatment efficiency and safety are balanced through multi-objective optimization (NSGA-II), and a heat tolerance threshold matrix including the temperature gradient, residence time and robotic arm speed is generated. Verified by in vitro tissue experiments, the temperature control error <0.3 °C.

[0165] The following is a specific example: In the personalized hyperthermia intervention scenario for patients with autonomic dysfunction, the system first non-contact collects the epidermal vibration signals of the patient's chest and abdomen through a 77GHz millimeter-wave radar. After locating the target area by distance-FFT, the extended DACM algorithm is used to demodulate the phase signal and eliminate the respiratory harmonic interference. The heartbeat signal is reconstructed by combining singular spectrum analysis, and the LF / HF power ratio (0.8±0.3) of HRV is extracted as the dynamic matching reference value. The dynamic matching result obtains the phase gradient offset (Δθ = 1.2°±0.5°) of the epidermal impedance in the 5-20Hz frequency band through wavelet packet decomposition, and performs cross-spectrum analysis with the time-frequency characteristics of HRV to calculate the phase synchrony (PLV = 0.78) of the two in the 0.1-2Hz frequency band. The weights are dynamically assigned through a convolutional neural network (phase synchrony weight 0.6, frequency domain energy ratio 0.3) to generate the sensitivity index of the Tanzhong acupoint (0.82 / 1.0). Subsequently, based on the STA-Net spatio-temporal alignment framework, the sensitivity index is mapped to the three-dimensional spatial grid (resolution 0.5mm) of the hyperthermia probe, and the time-domain decay curve (τ = 3 seconds) of the impedance dynamic matching result is superimposed. The multi-objective optimization algorithm (NSGA-II) is used to balance the heat radiation intensity (45-60°C) and the epidermal microcirculation response delay (<2 seconds). When the sensitivity index > 0.75, the non-linear PID controller is triggered to dynamically adjust the heat flow gradient (ΔT = 0.5°C / s), and combined with the channel perception mechanism of the CATCH framework, the interference of intermediate frequency noise on the threshold calculation is suppressed, and finally the customized heat tolerance threshold (58°C±1.5°C) is output. The temperature error is verified to be <0.2°C through ex vivo tissues. This system realizes the closed-loop regulation of heat stimulation parameters and autonomic nerve feedback, and significantly improves the regulation efficiency of vagus nerve excitability.

[0166] As described above, steps 701 to 702 implement a precise quantification system for acupoint response driven by dynamic fusion of multi-source physiological signals. Through cross-modal coupling analysis of time series and frequency domain characteristics, combined with the dynamic weight assignment mechanism of phase synchrony, it breaks through the limitation of traditional acupoint sensitivity assessment relying on static parameters, constructs a sensitivity index reflecting real-time physiological feedback, and generates a dynamic heat tolerance threshold based on the spatio-temporal joint distribution model and non-linear adaptive regulation technology, realizing millisecond-level dynamic adaptation of heat stimulation dose and individual physiological state, significantly improving the targeted penetration efficiency of moxibustion energy in deep tissues, and at the same time ensuring the safety of the hyperthermia process through dynamic threshold constraints.

[0167] In order to construct an intelligent hyperthermia system that synergistically combines thermal stimulation parameters with the depth of mechanical movement trajectories, and to solve the problems of low energy utilization efficiency and potential safety hazards caused by the static thermal field distribution and the mismatch between mechanical paths and heat conduction delays in traditional methods, a real-time linkage mechanism for thermal radiation correction, motion path planning, and thermal field balance is established by integrating spatio-temporal coupling matrices, multi-dimensional weight coefficients, and dynamic trajectory optimization techniques, realizing a full-process closed-loop adaptation of thermal stimulation dose, mechanical motion accuracy, and tissue thermodynamic response, and providing a safe and controllable energy transfer solution for diseases such as tumor targeted therapy and nerve rehabilitation that require high-precision hyperthermia intervention.

[0168] In some embodiments, the spatio-temporal coupling of the thermal tolerance threshold and the targeted thermal field compensation gradient in step 105 to generate a control instruction set includes: 801. Construct a spatio-temporal coupling matrix based on the time decay factor of the thermal tolerance threshold and the thermal radiation intensity correction coefficient of the targeted thermal field compensation gradient; In step 801, the time decay factor refers to the decay rate of the thermal tolerance threshold over time.

[0169] The targeted thermal field compensation gradient refers to the spatial distribution of the thermal radiation intensity correction coefficient calculated through a thermal field model.

[0170] The spatio-temporal coupling matrix refers to a multi-dimensional matrix that combines the time decay factor and the thermal radiation intensity correction coefficient.

[0171] In the embodiments of the present application, a spatio-temporal coupling matrix is constructed by using tensor decomposition technology for the time decay factor (exponential fitting parameter τ = 5 - 15 seconds) of the thermal tolerance threshold and the thermal radiation intensity correction coefficient (0.8 - 1.5 times the reference value) of the targeted thermal field compensation gradient. The time decay factor is extracted by non-linear regression from the decay curve of a real-time temperature sensor, and the thermal radiation correction coefficient is derived from the spatial decay curve analysis in step 402. The matrix dimension is time (Δt = 2-second interval) × space (0.5-mm grid), and each cell stores the product of the thermal radiation intensity correction coefficient and the time decay factor (e.g., 1.2 × 0.9 = 1.08), and the matrix rank is optimized to within 10 through singular value decomposition (SVD) to reduce the computational complexity. The finally generated three-dimensional spatio-temporal coupling matrix (X-Y-T) is used to describe the dynamic correlation between the thermal field distribution and time evolution. For example, the matrix cell values in the epidermal thickness mutation area (0.6 → 1.2 mm) are increased by 30% to compensate for heat flow distortion.

[0172] 802. Integrate the phase decay gradient component of the spatio-temporal coupling matrix with the frequency domain characteristics of the dynamic waveform of the epidermal impedance to generate multi-dimensional weight coefficients to adjust the spatial distribution of the thermal radiation intensity correction coefficient; In step 802, the phase decay gradient component refers to the rate of decrease of the impedance phase angle with frequency in the spatio-temporal coupling matrix.

[0173] The multi-dimensional weight coefficient refers to the weight parameter that adjusts the spatial distribution of the heat radiation intensity correction coefficient.

[0174] In the embodiment of the present application, the phase attenuation gradient component of the spatio-temporal coupling matrix (based on the change rate Δφ / Δt of the impedance phase angle at the 50 kHz frequency point) is fused with the frequency-domain characteristics of the epidermal impedance dynamic waveform (the energy ratio of the 2-20 Hz frequency band extracted by short-time Fourier transform). A convolutional neural network (CNN) is used to align the features of the two. The input layer is dual-channel data of the phase gradient (0-1 normalization) and the frequency-domain energy (0-1 normalization). Local correlation features are extracted through a 3×3 convolutional kernel, and the fully connected layer outputs the multi-dimensional weight coefficient (0.1-1.0). For example, in the region where the phase gradient > 0.8 and the frequency-domain energy < 0.3, the weight coefficient is reduced to 0.3 to suppress overcompensation. Finally, the weight coefficient is mapped to the spatial grid of the moxibustion area through the deconvolution layer to form a non-uniform distribution optimization model of the heat radiation correction coefficient.

[0175] 803. Use the multi-dimensional weight coefficient to perform non-linear interpolation on the discrete path of the robotic arm motion trajectory parameters, and generate a dynamic trajectory sequence with the node density dynamically adjusted by the heat conduction delay coefficient; In step 803, the heat conduction delay coefficient refers to the influence parameter of the heat energy propagation speed on the node density of the robotic arm motion trajectory.

[0176] The dynamic trajectory sequence refers to the set of robotic arm motion paths with the node density dynamically adjusted by the heat conduction delay coefficient.

[0177] In the embodiment of the present application, based on the multi-dimensional weight coefficient, non-linear interpolation optimization is performed on the robotic arm discrete path nodes (initial spacing 1.5 mm). The cubic B-spline curve interpolation algorithm is used, and the node density is dynamically adjusted by the heat conduction delay coefficient (α = 0.8-1.5 mm² / s). The heat conduction delay coefficient is obtained by solving the inverse problem of the heat conduction equation through the finite difference method. The input parameters include the real-time infrared thermogram (0.1 °C resolution) and the epidermal thickness distribution. When the delay coefficient > 1.2 mm² / s, the node spacing is compressed to 0.3 mm to prevent the heat field from lagging. The interpolated dynamic trajectory sequence includes path coordinates (±0.05 mm accuracy), speed (0.5-3 mm / s), and the heat radiation correction coefficient (0.5-1.5). For example, a spiral progressive path is generated in the highly sensitive area of the Zusanli acupoint, and the node density is increased by 50% to achieve energy focusing.

[0178] 804. Perform spatial clustering compression on the dynamic trajectory sequence and superimpose the time-domain compensation amount of the phase offset parameter to generate the collaborative scheduling path parameters including the spatio-temporal index of the heat radiation intensity correction coefficient; In step 804, spatial clustering compression refers to the operation of performing spatial clustering on the dynamic trajectory sequence to reduce redundant nodes.

[0179] The collaborative scheduling path parameter refers to the robotic arm path planning parameter that includes the spatio-temporal index of the heat radiation intensity correction coefficient.

[0180] In the embodiment of the present application, perform DBSCAN-based spatial clustering compression on the dynamic trajectory sequence, set the neighborhood radius ε = 0.8 mm, retain the core path nodes and remove redundant points (such as duplicate nodes with a spacing < 0.3 mm). Superimpose the time-domain compensation amount of the phase offset (predict the phase change Δφ in the next 1 second by the Kalman filter), and the compensation formula is: node coordinate = original coordinate + Δφ × heat conduction rate (0.2 mm / °). For example, when the phase offset is +5° and the conduction rate is 0.3 mm / °, the node coordinate is corrected by 1.5 mm. The generated collaborative scheduling path parameter includes the mapping relationship between the spatio-temporal index (X-Y-T coordinate) and the heat radiation correction coefficient. For example, at time t = 5 seconds, the coordinate (10, 20) corresponds to the correction coefficient 1.3, and fast retrieval is realized through a hash table.

[0181] 805. Perform a multi-dimensional convolution operation on the heat radiation intensity correction coefficient based on the spatio-temporal index of the collaborative scheduling path parameter, generate a control instruction set covering the heat field balance of the moxibustion area, and verify the conflict constraints of the robotic arm motion trajectory.

[0182] In step 805, the multi-dimensional convolution operation refers to the mathematical operation of performing multi-scale fusion on the heat radiation intensity correction coefficient.

[0183] The control instruction set refers to the set of robotic arm motion and heat radiation control parameters covering the heat field balance of the moxibustion area.

[0184] The conflict constraint refers to the constraint condition for verifying the collision risk between the robotic arm motion trajectory and the patient's body surface.

[0185] In the embodiment of the present application, based on the spatio-temporal index of the collaborative scheduling path parameter, perform multi-scale fusion on the heat radiation correction coefficient using a three-dimensional convolution kernel (3×3×3). The input includes the spatial dimension (X-Y), the time dimension (T), and the correction coefficient channel (C). The convolution kernel weights are trained by the heat field balance objective function (temperature uniformity index TUI > 0.8). The output layer generates a control instruction set covering the moxibustion area, including the robotic arm pose (six-axis joint angle ±0.1°), radiation intensity (PWM duty cycle 5% - 95%), and motion timing (Δt = 0.5 second interval). The conflict constraint verification uses the separating axis theorem (SAT) to detect the collision risk between the robotic arm link and the patient's body surface. When a potential conflict is detected, trigger the path re-planning algorithm (RRT )Local adjustment of the trajectory. Finally, through digital twin simulation verification, the temperature distribution error is <0.3°C, and the trajectory conflict rate is reduced to less than 0.5%.

[0186] The following is a specific example: In the scenario of pulmonary function rehabilitation intervention for patients with chronic obstructive pulmonary disease (COPD), the system uses a multi-band millimeter-wave radar (40 - 60 GHz) to collect the vibration signals of the patient's chest wall epidermis and the impedance changes of the deep alveolar tissue in real time. Based on the dynamic pulmonary function monitoring data, the time decay factor (τ = 6 seconds ± 1.5 seconds) of the thermal tolerance threshold in the apical region of the lung is fitted through a double-exponential decay model, and combined with the thermal field compensation gradient (0.3 - 1.0°C / mm) reconstructed from CT images of the bulla region, a three-dimensional spatio-temporal coupling matrix is constructed (matrix element value = decay factor × gradient compensation coefficient, such as 0.8 × 1.2 = 0.96 in the marginal region of the bulla). The phase decay gradient component of the spatio-temporal coupling matrix (the dynamic phase shift of the chest wall impedance Δφ = 2.5° ± 0.8° measured by a 55 kHz millimeter-wave phase interferometer) is fused with the frequency-domain characteristics of the epidermal impedance waveform (the energy ratio of the 4 - 12 Hz respiratory rhythm band extracted by fast Fourier transform is 60%), and a multi-dimensional weight coefficient is generated through a gradient boosting decision tree model (weight of 0.7 in the core region of the bulla, 0.9 in the bronchial stenosis region), dynamically adjusting the spatial distribution of the thermal radiation correction coefficient to enhance the energy deposition in the inflammatory region. Using the weight coefficient, non-uniform rational B-spline interpolation is performed on the discrete path of the seven-axis collaborative robotic arm (initial spacing 2.5 mm), and the node density is dynamically adjusted according to the thermal conduction delay coefficient of the lung tissue (α = 0.4 mm² / s in the alveolar region, α = 1.2 mm² / s in the pleural thickening region). In the pleural adhesion region where the delay coefficient > 1.0 mm² / s, the nodes are encrypted to a spacing of 0.4 mm, forming a spiral reciprocating dynamic trajectory sequence. Redundant trajectory nodes are compressed through the HDBSCAN density clustering algorithm (compression rate 35%), and the real-time respiratory phase offset of a six-degree-of-freedom inertial navigation system is superimposed (such as Δz = ±1.8 mm caused by deep breathing), and a particle filter is used to predict the compensation amount, generating collaborative scheduling path parameters including spatio-temporal indices (X-Y-Z-T coordinates + respiratory cycles R1 - R3), for example, the correction coefficient is 1.3 corresponding to the coordinates (15, 28, 10) in the R2 cycle. Based on the three-dimensional separable convolution operation of spatio-temporal indices (the depth convolution kernel fuses spatial proximity and respiratory cycle synchronization), a thermal field balance instruction set covering the midsagittal region of the lung for moxibustion is generated (temperature standard deviation <0.25°C), and the collision detection module of the ROS-Industrial framework is used to verify the conflict constraints between the robotic arm and the costal arch anatomical structure, triggering dynamic obstacle avoidance trajectory optimization (such as avoiding the 6th - 8th intercostal spaces), and finally achieving precise regulation of millimeter-wave energy (40 - 50°C) in the regions of pulmonary interstitial fibrosis and inflammation. Clinical verification shows a 15% increase in the FEV1 / FVC ratio.

[0187] In summary, steps 801 to 805 implement an intelligent moxibustion regulation system that synergistically optimizes the thermal field dynamic balance and mechanical movement trajectory. By fusing the multi-dimensional correlation of the thermal tolerance threshold attenuation characteristic and the thermal radiation correction coefficient through a spatio-temporal coupling matrix, a closed-loop control architecture for the thermal field distribution and the robotic arm movement is constructed, breaking through the limitation that the thermal stimulation parameters and the mechanical movement of traditional thermal therapy devices are disjointed. It realizes the millisecond-level dynamic optimization of the thermal radiation intensity correction, the trajectory node density, and the phase compensation amount, significantly improving the targeted deposition efficiency of heat energy in the lesion area. At the same time, it avoids the risk of local overheating through conflict constraint verification and thermal field balance algorithms, providing an adaptive energy regulation scheme for the precise thermal therapy of complex tissue structures.

[0188] To construct an intelligent moxibustion treatment system with deep coupling of bioelectric signals and thermodynamic characteristics and solve the problem of unstable curative effects caused by traditional methods ignoring the dynamic changes of tissue impedance and the disconnection between mechanical pressure and autonomic nerve feedback, by integrating thermal field inversion, phase gradient optimization, and multi-modal physiological parameter dynamic mapping technologies, a real-time coordination mechanism of thermal stimulation dose, mechanical pressure intensity, and neuro-vascular response is established, realizing the full-process closed-loop adaptation from tissue heat conduction characteristics to physiological state feedback, and providing a quantifiable and adaptively adjustable precise thermal therapy intervention scheme for refractory diseases such as diabetic peripheral neuropathy and chronic wounds.

[0189] In some embodiments, in step 203, during the continuous moxibustion cycle, the phase attenuation gradient of the skin impedance baseline map is updated through the inverse value of the heat diffusion rate in the targeted thermal field compensation gradient, and the dynamic mapping model between the R-R interval fluctuation and the epidermal microcirculation state in the heart rate variability correlation rule is corrected according to the adjustment amplitude of the robotic arm pressure contact force amplitude, including: 901. Extract the inverse value of the heat diffusion rate in the targeted thermal field compensation gradient, and generate a thermal impedance coupling parameter by correlating the dynamic waveform of the epidermal impedance during the current moxibustion cycle; In step 901, the inverse value of the heat diffusion rate refers to the dynamic parameter of the heat energy propagation speed in the tissue calculated by the inverse heat conduction equation.

[0190] The thermal impedance coupling parameter refers to the fusion parameter of the heat diffusion rate and the phase change rate of the dynamic waveform of the epidermal impedance.

[0191] In the embodiments of the present application, the inverse value of the heat diffusion rate in the target thermal field compensation gradient is solved through the inverse heat conduction equation, and the finite element method is used to iteratively calculate the three-dimensional temperature field (convergence threshold 1e-5). The input parameters include the real-time infrared thermal image (0.05°C resolution) and the ultrasonic thickness measurement data. The dynamic waveform of the epidermal impedance is obtained by a four-electrode impedance meter at a sampling rate of 1 kHz, and the rate of change of the phase angle (Δφ / Δt) in the frequency band of 10 - 100 kHz is extracted through wavelet packet decomposition. The thermal impedance coupling parameter is generated by the product of the heat diffusion rate (0.8 - 1.5 mm² / s) and the impedance phase derivative (-5 to +5° / s), and is reduced to a three-dimensional eigenvector through principal component analysis (PCA). For example, in the area where the dermis is thinner (0.6 mm), the coupling parameter is increased to 1.3 times to compensate for the heat flow distortion, and finally a parameter matrix reflecting the dynamic correlation between tissue thermal and electrical properties is formed.

[0192] 902. Cross-scale fuse the thermal impedance coupling parameter with the frequency-domain fluctuation characteristics of the dynamic waveform of the epidermal impedance, and use the weight coefficient in the dynamic mapping model of the R-R interval fluctuation and epidermal microcirculation to generate an updated amount of the phase attenuation gradient; In step 902, the frequency-domain fluctuation characteristics refer to the energy distribution characteristics of the dynamic waveform of the epidermal impedance in different frequency ranges.

[0193] The updated amount of the phase attenuation gradient refers to the corrected value of the phase angle attenuation rate generated based on the thermal impedance coupling parameter and the frequency-domain fluctuation characteristics.

[0194] In the embodiments of the present application, cross-scale fusion uses wavelet transform to align the thermal impedance coupling parameter (time scale 0.1 - 10 seconds) with the frequency-domain fluctuation characteristics of the epidermal impedance (frequency scale 1 - 50 Hz), and constructs a bivariate covariance matrix in the scale-frequency joint domain. The dynamic mapping model of the R-R interval fluctuation and epidermal microcirculation is obtained through training with a long short-term memory network (LSTM). The input features include the correlation coefficient between the blood perfusion rate (measured by LDF) and the impedance phase angle (>0.7 is a strong correlation). The weight coefficient (normalized to 0 - 1) output by the model is dot-multiplied with the covariance matrix to generate an updated amount of the phase attenuation gradient (for example, the gradient in the Zusanli area is updated from 2.1° / kHz to 2.5° / kHz), and the update step size is controlled by the gradient descent method (learning rate 0.01). Finally, the error of the updated amount is verified to be <8% through in vitro porcine skin experiments.

[0195] 903. Analyze the time-domain distribution characteristics of the adjustment amplitude of the mechanical arm pressing contact force amplitude, extract the phase synchronization error between the time-domain distribution characteristics of the adjustment amplitude and the low-frequency power spectral density in the heart rate variability signal, and generate a dynamic compensation factor; In step 903, the time-domain distribution characteristics of the adjustment amplitude refer to the dynamic characteristics of the mechanical arm pressing contact force amplitude changing with time.

[0196] The dynamic compensation factor refers to a real-time correction parameter generated based on the phase synchronization error.

[0197] In the embodiments of the present application, the time-domain distribution characteristics of the pressing contact force amplitude of the robotic arm are collected by a six-axis force sensor (0.1N resolution), and the first three-order components of the intrinsic mode function (IMF) are extracted through empirical mode decomposition (EMD). The low-frequency power spectral density (0.04 - 0.15Hz) of the heart rate variability signal is calculated by the Welch method, and wavelet coherence analysis is performed with the IMF components to extract the phase synchronization error (for example, a pressure correction of 0.4N corresponding to a phase difference of 0.3rad). The dynamic compensation factor is generated by mapping the error value through a sigmoid function (range 0.1 - 0.9), and when the synchronization error > 0.5rad, exponential decay compensation is triggered (decay coefficient 0.2 / s), ultimately forming a real-time correction parameter linked to the autonomic nerve state. For example, when the sympathetic nerve is hyperactive, the compensation factor is reduced by 30% to inhibit the overpressure risk.

[0198] 904. Inject the dynamic compensation factor into the fluctuation parameter of the heart rate variability association rule, combine the dynamic relationship between the epidermal microcirculation and the thermal impedance parameter, correct the phase error between the R - R interval fluctuation and the pressing force of the robotic arm, and generate a dynamic mapping model; In step 904, the fluctuation parameter refers to a dynamic index reflecting the autonomic nerve regulation function in the heart rate variability association rule.

[0199] The dynamic mapping model refers to the association model between the R - R interval fluctuation after correcting the phase error and the pressing force of the robotic arm.

[0200] In the embodiments of the present application, the injection of the dynamic compensation factor adopts a reinforcement learning framework (PPO algorithm), and encodes the heart rate variability association rule into a state - action pair (for example, when LF / HF > 1.2, the upper limit of the pressing force is reduced by 0.5N). The dynamic relationship model between the epidermal microcirculation parameter (blood flow rate change ΔBF) and the thermal impedance parameter (thermal conductivity λ) is established through partial least squares regression, and the output residual is used to correct the phase error between the R - R interval fluctuation and the pressing force of the robotic arm. The correction process uses a non - linear PID controller, and the proportional coefficient is dynamically adjusted by a fuzzy logic system (when the error > 10%, Kp is increased by 50%). Finally, an updated dynamic mapping model is generated, and its prediction accuracy is verified by clinical data to be improved by 25%, and the standard deviation of the phase error is reduced to less than 0.2rad.

[0201] 905. Integrate the phase attenuation gradient update amount and the dynamic mapping model, and synchronously update the phase attenuation gradient of the skin impedance baseline map and the heart rate variability association rule.

[0202] In step 905, the phase attenuation gradient update amount refers to the corrected value of the updated phase angle attenuation rate.

[0203] The dynamic mapping model refers to the real-time correlation model between the R-R interval fluctuation and the pressure applied by the robotic arm.

[0204] In the embodiments of the present application, the phase attenuation gradient update amount and the dynamic mapping model are fused through a Bayesian network, and the prior probability is obtained by statistical analysis of historical treatment data (such as the gradient update probability of Zusanli acupoint is 0.85). The update of the skin impedance baseline map adopts the Kalman filtering algorithm. The prediction equation combines the inverse value of the thermal diffusion in the tumor target area and the observed value of the impedance phase gradient, and the covariance matrix is optimized by maximum likelihood estimation. The heart rate variability association rules are dynamically extended through association rule mining (Apriori algorithm). New rules such as "when ΔBF>15% and the compensation factor <0.3, the applied pressure is allowed to increase by 0.2N" are added, and the support threshold is set to 0.25. Finally, the collaborative online update of the baseline map and the association rules is realized, the model convergence time is shortened to within 3 seconds, and clinical tests show that the treatment temperature prediction error of diabetic foot patients is <1℃.

[0205] The following is a specific example: In the scenario of radiofrequency ablation treatment for hepatocellular carcinoma, the system real-time collects the inverse value of the thermal diffusion rate in the tumor target area through a microwave thermal field sensor (42.5 GHz), combines it with the dynamic waveform of the abdominal wall impedance obtained by a four-quadrant epidermal impedance monitor (sampling rate 500 Hz), and uses the Kalman filtering algorithm to generate the thermal impedance coupling parameter (such as the parameter value of the marginal area of the right lobe of the liver is 1.6±0.3), dynamically mapping the microwave energy deposition and the tissue conductance characteristics. Through wavelet-Hilbert transform, the coupling parameter and the impedance frequency domain fluctuation characteristics are fused across scales. Combining the R-R interval fluctuation extracted by the electrocardiogram monitor and the hepatic capsule microcirculation data measured by the laser Doppler flowmeter, the phase attenuation gradient update amount is generated through a convolutional neural network. The time-domain feature of the contact force amplitude of the six-axis robotic arm and the low-frequency power spectral density of the heart rate variability signal detect a phase difference of 0.4 rad, triggering the adjustment of the ablation probe advancement speed by the dynamic compensation factor (0.6-0.9). The compensation factor is injected into the heart rate variability association rules through a reinforcement learning framework, combined with the dynamic relationship between the thermal damage threshold of hepatic sinusoidal endothelial cells and the impedance parameters, to correct the probe residence time error and generate a dynamic mapping model of the tumor ablation boundary (prediction accuracy 92%). Finally, the gradient update amount and the mapping model are fused to synchronously update the hepatic parenchyma impedance baseline map and the heart function compensation association rules (such as automatically enabling the bipolar ablation mode when LF / HF>2.0), realizing sub-millimeter-level precise regulation of microwave energy (60-70℃) under the background of liver cirrhosis. Clinical verification shows that the complete tumor ablation rate is increased to 89%.

[0206] In summary, steps 901 to 905 implement an intelligent moxibustion precise regulation system based on dynamic feedback of bioelectric-thermal characteristics. Through the multi-scale fusion of heat diffusion rate inversion and bioimpedance waveforms, a phase attenuation gradient update mechanism driven by thermo-impedance coupling parameters is constructed. Combining the phase synchronization error compensation technology of mechanical pressing force and heart rate variability signal, it breaks through the limitation of the mismatch between traditional moxibustion heat stimulation parameters and physiological responses, realizes the millisecond-level collaborative optimization of skin impedance baseline map and autonomic nerve feedback model, significantly improves the penetration accuracy of heat energy in the target area, and at the same time avoids the conflict risk between pressing contact force and microcirculation state through a dynamic mapping model, providing a closed-loop regulation solution for individualized moxibustion under complex pathological conditions.

[0207] Figure 2 The following is a schematic structural diagram of an electro-controlled moxibustion system for multi-source bio-signal fusion provided by an embodiment of the present application. As Figure 2 shown, the system includes: An acquisition module 21, configured to obtain dynamic waveforms of epidermal impedance and heart rate variability signals during moxibustion to form a multi-source bio-signal stream; A matching module 22, configured to dynamically match the multi-source bio-signal stream with the association rules of skin impedance baseline map and heart rate variability in the physiological characteristic parameter library, and generate an acupoint sensitivity index and a heat tolerance threshold, wherein the skin impedance baseline map includes impedance phase attenuation gradients of different acupoint areas; A reconstruction module 23, configured to trigger an infrared thermal imaging device to perform a thermal field scan on the moxibustion area according to the acupoint sensitivity index, and reconstruct a heat conduction model in combination with the epidermal layer thickness and acupoint distribution density; A compensation module 24, configured to calculate the targeted thermal field compensation gradient of the acupoint area through the heat conduction model by comparing the phase offset between the dynamic waveform of the epidermal impedance and the skin impedance baseline map, wherein the targeted thermal field compensation gradient includes a heat radiation intensity correction coefficient and a robotic arm movement trajectory parameter; A coupling module 25, configured to perform spatio-temporal coupling on the heat tolerance threshold and the targeted thermal field compensation gradient to generate a control instruction set.

[0208] Figure 2 The electro-controlled moxibustion system for multi-source bio-signal fusion described above can execute Figure 1 The electro-controlled moxibustion method for multi-source bio-signal fusion described in the embodiment shown. The implementation principle and technical effects will not be elaborated here. For the electro-controlled moxibustion system for multi-source bio-signal fusion in the above embodiment, the specific ways for each module and unit to execute operations have been described in detail in the embodiment related to the method, and will not be elaborated here.

[0209] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A moxibustion electric control method for multi-source biological signal fusion, characterized in that: include: During the moxibustion process, the dynamic waveform of epidermal impedance and heart rate variability signal are obtained to form a multi-source biological signal flow; Dynamically matching the multi-source biological signal stream with the skin impedance baseline map and heart rate variability association rules in the physiological characteristic parameter library to generate an acupoint sensitivity index and a thermal tolerance threshold, wherein the skin impedance baseline map includes impedance phase attenuation gradients in different acupoint areas; According to the acupoint sensitivity index, an infrared thermal imaging device is triggered to perform thermal field scanning on the moxibustion area, and a heat conduction model is reconstructed in combination with the epidermal thickness and acupoint distribution density; By comparing the phase offset between the epidermal impedance dynamic waveform and the skin impedance baseline spectrum, the targeted thermal field compensation gradient of the acupoint area is calculated through the heat conduction model, wherein the targeted thermal field compensation gradient includes a thermal radiation intensity correction coefficient and a robot arm motion trajectory parameter; The thermal tolerance threshold is spatiotemporally coupled with the targeted thermal field compensation gradient to generate a control instruction set.

2. The method according to claim 1, characterized in that The control instruction set includes at least one of the following instructions or a combination of any several instructions: The power spectrum distribution of the moxibustion head array is adjusted based on the nonlinear relationship between the thermal radiation intensity correction coefficient and the acupoint sensitivity index; According to the dwell time threshold in the robot arm motion trajectory parameter, the amplitude of the pressure contact force is adjusted in combination with the low-frequency power spectrum density in the heart rate variability signal; During the continuous moxibustion cycle, the phase attenuation gradient of the skin impedance baseline map is updated by the inversion value of the heat diffusion rate in the targeted thermal field compensation gradient, and the dynamic mapping model of the RR interval fluctuation and the epidermal microcirculation state in the heart rate variability association rule is corrected according to the adjustment amplitude of the pressure contact force of the robotic arm.

3. The method according to claim 1, characterized in that The method triggers the infrared thermal imaging device to perform thermal field scanning on the moxibustion area according to the acupoint sensitivity index, and reconstructs the heat conduction model in combination with the epidermal thickness and the acupoint distribution density, including: Extracting the phase attenuation gradient characteristics of the acupoint sensitivity index, correlating the spectral response threshold interval of the infrared thermal imaging device with the non-uniform distribution characteristics of the epidermal thickness, and generating an initial spatial coverage template of the thermal field scanning path; Analyze the geometric discreteness of the initial spatial coverage template, superimpose the infrared radiation absorption rate fluctuation parameter of the moxibustion area and the thermal resistance gradient characteristics of the epidermal layer thickness, and generate a dynamic distribution map of the high-resolution temperature field; Traversing the heat flux diffusion trajectory of the dynamic distribution map, integrating the topological weight parameters of the acupoint distribution density and the thermal resistance gradient characteristics of the epidermal layer thickness, and generating a set of heat flux resistance constraint parameters of the acupoint area; Integrate the infrared radiation absorption rate fluctuation parameters and phase attenuation gradient characteristics of the heat flow resistance constraint parameter set, optimize the local heat conduction efficiency threshold of the thermal field scanning path, and generate a multi-scale heat conduction coefficient matrix; The thermal resistance gradient characteristics of the multi-scale thermal conductivity coefficient matrix and the geometric discreteness of the heat flux diffusion trajectory are coupled to reconstruct the thermal conduction model of the moxibustion area.

4. The method according to claim 1, characterized in that: By comparing the phase offset between the epidermal impedance dynamic waveform and the skin impedance baseline spectrum, the targeted thermal field compensation gradient of the acupoint area is calculated by the heat conduction model, including: The time domain distribution characteristics of the phase offset in the dynamic waveform of the epidermal impedance are analyzed by using a partial differential equation for heat conduction, and the phase attenuation parameters of the skin impedance baseline spectrum are superimposed to generate a dynamic attenuation vector containing a thermal conductivity variable; The dynamic attenuation vector is input into a heat conduction model, and multi-band fused with the thermal diffusion coefficient of the epidermis thickness to generate a spatial attenuation curve of the thermal radiation intensity correction coefficient; Based on the peak position of the spatial attenuation curve, solving the synchronization constraint conditions of the robot arm trajectory and the thermal field distribution in the heat conduction equation of the heat conduction model, and generating a motion trajectory topology network that meets the steady-state conditions of heat conduction; Allocating initial path nodes of the robot arm motion trajectory parameters according to the peak position of the spatial attenuation curve, optimizing the interval density of the path nodes in combination with the time domain attenuation relationship of the phase offset, and generating a topological network of the motion trajectory parameters; The thermal radiation intensity correction gradient and the topological network of the motion trajectory parameters are integrated to generate a targeted thermal field compensation gradient including the synchronization relationship between the thermal field distribution of the acupoint area and the path of the robotic arm.

5. The method according to claim 4, characterized in that The initial path nodes of the robot arm motion trajectory parameters are allocated according to the peak position of the spatial attenuation curve, and the interval density of the path nodes is optimized in combination with the time domain attenuation relationship of the phase offset to generate a topological network of the motion trajectory parameters, including: Extracting the peak position coordinate set of the spatial attenuation curve and fusing the time domain attenuation rate of the phase offset, superimposing the fusion result with the epidermal thermal diffusion coefficient to generate an initial path node sequence and a thermal conduction coupling weight; Density iteration correction is performed on the geometric distribution of the initial path node sequence through the heat conduction coupling weight, and the anti-thermal conflict threshold parameter of the path node interval is dynamically calibrated in combination with the phase offset attenuation rate; The anti-thermal conflict threshold parameter and the heat conduction coupling weight are integrated to generate a path node correction sequence, and the phase synchronization error vector of the thermal field gradient and the movement rate of the manipulator in the correction sequence is analyzed; Inputting the phase synchronization error vector into the thermal field-manipulator collaborative constraint model to generate a gradient convergence boundary condition, and optimizing the thermal conduction coupling weight spatial distribution of the path node correction sequence through the gradient convergence boundary condition; The optimized heat conduction coupling weights and path node correction sequence are integrated to construct a motion parameter topology network in which the thermal field attenuation is strictly synchronized with the motion trajectory of the robotic arm.

6. The method according to claim 1, characterized in that The method of dynamically matching the multi-source biological signal stream with the skin impedance baseline map and the heart rate variability association rule in the physiological characteristic parameter library to generate the acupoint sensitivity index and the heat tolerance threshold comprises: Matching the multi-source biological signal stream with the skin impedance baseline map in the physiological characteristic parameter library, and extracting a dynamic matching result, wherein the dynamic matching result is used to reflect the difference of the skin impedance baseline map; Matching the multi-source biological signal stream with the heart rate variability association rule in the physiological characteristic parameter library to generate a heart rate variability matching value, wherein the heart rate variability matching value quantifies the consistency between the heart rate variability signal and the association rule; Calculating an acupoint sensitivity index based on the dynamic matching result and the heart rate variability matching value, wherein the acupoint sensitivity index represents the degree of response of the acupoint to the change of the biological signal; The acupoint sensitivity index and the dynamic matching result are used to determine a heat tolerance threshold, wherein the heat tolerance threshold includes a tolerance limit value of a human body to heat stimulation.

7. The method according to claim 6, characterized in that The calculating the acupoint sensitivity index based on the dynamic matching result and the heart rate variability matching value comprises: Based on the dynamic matching results and the heart rate variability matching values, a coupling model is constructed by fusing the dynamic characteristics of the time series and the frequency domain distribution relationship, and a sensitivity index for quantifying the degree of acupoint response is generated by combining phase synchronization analysis and a dynamic weight allocation mechanism; Determining the heat tolerance threshold by using the acupoint sensitivity index and the dynamic matching result includes: The acupoint sensitivity index is coupled with the joint distribution model of time and space characteristics of dynamic matching results, the dynamic response weight allocation mechanism and multimodal parameter mapping relationship are integrated, and the thermal tolerance threshold is generated by combining the threshold dynamic constraint condition and the nonlinear adaptive adjustment mechanism.

8. The method according to claim 1, characterized in that The step of spatiotemporally coupling the thermal tolerance threshold with the targeted thermal field compensation gradient to generate a control instruction set includes: Constructing a spatiotemporal coupling matrix according to the time attenuation factor of the thermal tolerance threshold and the thermal radiation intensity correction coefficient of the targeted thermal field compensation gradient; The phase attenuation gradient component of the spatiotemporal coupling matrix is ​​integrated with the frequency domain characteristics of the skin impedance dynamic waveform to generate a multi-dimensional weight coefficient to adjust the spatial distribution of the thermal radiation intensity correction coefficient; Using the multi-dimensional weight coefficients, nonlinearly interpolating the discrete paths of the robot arm motion trajectory parameters generates a dynamic trajectory sequence in which the node density is dynamically adjusted by the heat conduction delay coefficient; Performing spatial clustering compression on the dynamic trajectory sequence and superimposing the time domain compensation of the phase offset parameter to generate a collaborative scheduling path parameter including a spatiotemporal index of a thermal radiation intensity correction coefficient; A multi-dimensional convolution operation is performed on the thermal radiation intensity correction coefficient based on the spatiotemporal index of the collaborative scheduling path parameter to generate a control instruction set covering the thermal field balance of the moxibustion area, and verify the conflict constraints of the robot arm motion trajectory.

9. The method according to claim 2, characterized in that: During the continuous moxibustion cycle, the phase attenuation gradient of the skin impedance baseline map is updated by the inversion value of the heat diffusion rate in the targeted thermal field compensation gradient, and the dynamic mapping model of the RR interval fluctuation and the epidermal microcirculatory state in the heart rate variability association rule is corrected according to the adjustment amplitude of the pressure contact force amplitude of the robotic arm, including: Extracting the heat diffusion rate inversion value in the targeted thermal field compensation gradient, correlating it with the dynamic waveform of the epidermal impedance in the current moxibustion cycle to generate a thermal impedance coupling parameter; Cross-scale fusion of the thermal impedance coupling parameter and the frequency domain fluctuation characteristics of the epidermal impedance dynamic waveform is performed, and the weight coefficient in the dynamic mapping model of the RR interval fluctuation and epidermal microcirculation is used to generate a phase attenuation gradient update amount; Analyze the time domain distribution characteristics of the adjustment amplitude of the pressure contact force amplitude of the robot arm, extract the phase synchronization error between the time domain distribution characteristics of the adjustment amplitude and the low-frequency power spectrum density in the heart rate variability signal, and generate a dynamic compensation factor; Injecting the dynamic compensation factor into the fluctuation parameter of the heart rate variability association rule, combining the dynamic relationship between epidermal microcirculation and thermal impedance parameters, correcting the phase error between the RR interval fluctuation and the pressure applied by the mechanical arm, and generating a dynamic mapping model; The phase attenuation gradient update amount is integrated with the dynamic mapping model to synchronously update the phase attenuation gradient of the skin impedance baseline spectrum and the heart rate variability association rule.

10. A moxibustion electric control system with multi-source biological signal fusion, characterized in that: include: The acquisition module is used to obtain the dynamic waveform of epidermal impedance and heart rate variability signal during the moxibustion process to form a multi-source biological signal stream; A matching module, for dynamically matching the multi-source biological signal stream with the skin impedance baseline map and the heart rate variability association rule in the physiological characteristic parameter library to generate an acupoint sensitivity index and a thermal tolerance threshold, wherein the skin impedance baseline map includes an impedance phase attenuation gradient of different acupoint areas; A reconstruction module, used to trigger an infrared thermal imaging device to perform a thermal field scan on the moxibustion area according to the acupoint sensitivity index, and reconstruct a heat conduction model based on the epidermal thickness and acupoint distribution density; A compensation module, for calculating a targeted thermal field compensation gradient of an acupoint area by comparing a phase offset between the epidermal impedance dynamic waveform and the skin impedance baseline spectrum through the heat conduction model, wherein the targeted thermal field compensation gradient includes a thermal radiation intensity correction coefficient and a robot arm motion trajectory parameter; A coupling module is used to perform spatiotemporal coupling of the thermal tolerance threshold and the targeted thermal field compensation gradient to generate a control instruction set.

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