Personalized health consultation service system based on traditional Chinese medicine health preserving theory

Abstract

The invention discloses a personalized health consultation system based on a traditional Chinese medicine health preserving theory, which is applied to the technical field of intelligent medical treatment and traditional Chinese medicine digitization and solves the problem that multi-mode traditional Chinese medicine physical sign quantitative acquisition and syndrome differentiation cannot be realized in health consultation. The system comprises a front-end health acquisition intervention terminal and a cloud intelligent analysis platform, the front end comprises a multi-modal acquisition unit for high-dimensional inspection, gas-phase auscultation, pulse-taking and the like and a non-contact body surface stimulation unit; a CNN, GNN and Transform mixed deep learning model is set at the cloud end, and traditional Chinese medicine knowledge graph constraint reasoning, multi-source simultaneous acquisition, encrypted transmission tensor fusion and fractional order pulse condition analysis are introduced; based on a quantum harmonic oscillator model, a personalized scheme of diet regulation and intake and spectrum resonance stimulation parameters is set, and objective and personalized traditional Chinese medicine diagnosis and precise intervention are realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent medical technology, and more specifically to a personalized health consultation service system based on traditional Chinese medicine health preservation theory. Background Technology

[0002] For a long time, TCM health management has suffered from a lack of objectivity and standardization in diagnosis. The four diagnostic methods of observation, auscultation, inquiry, and palpation rely on the subjective judgment of physicians, resulting in inconsistent and inaccurate judgment standards. This has severely hampered the large-scale and precise development of TCM health preservation services.

[0003] Currently, several electronic devices attempting to objectify TCM diagnosis are widely used in TCM health management. Devices using ordinary color cameras for tongue and facial imaging cannot acquire multispectral information reflecting subcutaneous Qi and blood status and are easily affected by ambient light, resulting in limited feature extraction dimensions. Devices using single gas sensors for auscultation have poor resolution of multiple characteristic volatiles in complex exhaled gases, exhibiting high cross-sensitivity and low detection accuracy. In pulse diagnosis, most existing pulse diagnostic instruments use only single-point or single-dimensional pressure sensors, failing to simultaneously acquire the pressure at the "cun," "guan," and "chi" pulse positions, vascular wall compliance, and local temperature field. Furthermore, they lack an active pressure mechanism to simulate the dynamic changes of traditional palpation techniques, leading to significant loss of pulse information. Regarding data analysis methods, existing systems mostly isolate various vital sign data, lacking a cross-modal correlation analysis framework. They use shallow machine learning models to mine complex physiological relationships hidden in high-dimensional heterogeneous data, and the reasoning process is not connected to the theoretical systems of Yin-Yang and Five Elements, Zang-Fu organs, and meridians in TCM, failing to achieve reliable mapping and interpretation from modern data features to TCM syndrome elements. In addition, existing health consultation systems suffer from a disconnect between diagnosis and treatment. They primarily provide lifestyle advice such as diet and exercise, but lack terminal devices that can directly apply precise, non-invasive physical interventions based on the diagnosis results, thus failing to form a service loop of "data collection - status identification - precise intervention - effect evaluation".

[0004] Therefore, developing an intelligent health consultation service system that can simultaneously and accurately collect and deeply integrate multimodal TCM vital signs and combine them with targeted physical intervention is an inevitable requirement for breaking through current technological bottlenecks and realizing the modernization of TCM health preservation services. Summary of the Invention

[0005] To address the technical problems of existing TCM diagnostic equipment, such as limited data acquisition dimensions, insufficient multimodal data fusion, disconnect between syndrome differentiation and theory, and inability to provide precise intervention, this paper proposes a personalized health consultation service system based on TCM health preservation theory. This system achieves closed-loop service through a front-end terminal embedding multimodal acquisition and dynamic feedback modules and a cloud-based intelligent analysis platform. The front-end terminal employs a high-dimensional observation unit composed of a multispectral imaging camera and a depth camera, a multi-channel gas sensor array with microfluidic gas pathways for auscultation, and a biomimetic pulse diagnosis wristband using a micro-pressure sensor array, bioimpedance sensors, and micro-linear actuators for multi-dimensional pulse acquisition. The cloud platform constructs a hybrid model based on convolutional neural networks, graph neural networks, and Transformers, constrained by a TCM knowledge graph, to achieve multimodal feature tensor fusion and syndrome differentiation reasoning. This generates dietary regulation plans and personalized health consultation plans using non-contact surface stimulation parameters. Targeted physical intervention is achieved through axial resonators and spectral stimulation lamps, forming a technical path from multi-source data acquisition and intelligent syndrome differentiation to precise intervention, improving the objectivity, accuracy, and systematization of TCM health status identification and regulation.

[0006] To achieve the above-mentioned technical effects, the present invention adopts the following technical solution: A personalized health consultation service system based on traditional Chinese medicine health preservation theory includes a front-end health data collection and intervention terminal and a cloud-based intelligent analysis platform that interacts with the terminal via a network. The front-end health acquisition and intervention terminal includes an integrated main shell, a main control module and a three-dimensional calibration module disposed inside the main shell, and a multimodal acquisition module and a dynamic feedback module that are respectively communicatively connected to the main control module; The multimodal acquisition module includes a high-dimensional observation unit, a gas phase auscultation unit, and a pulse diagnosis unit. The high-dimensional observation unit is mounted on the front of the main housing via a motorized gimbal, which is electrically connected to the main control module. The high-dimensional observation unit includes a multispectral imaging camera, a depth camera, and a ring-shaped uniform light source surrounding the lens. The gas phase auscultation unit includes a microfluidic gas path system embedded in the main housing, comprising an air inlet, a micro air pump, a multi-channel gas sensor array, and a gas path cleaning device. A replaceable disposable mouthpiece is provided at the air inlet. The pulse diagnosis unit includes a bionic pulse diagnosis wristband detachably connected to the main housing via a data cable. The bionic pulse diagnosis wristband includes a ring-shaped flexible baseband, a micro-pressure sensor array embedded in the inner side of the flexible baseband forming three sections ("cun," "guan," and "chi"), and bioimpedance sensors and temperature sensor arrays distributed around each micro-pressure sensor array. A micro linear actuator is integrated inside the bionic pulse diagnosis wristband. The dynamic feedback module includes a non-contact surface stimulation unit integrated within the main housing, the unit including an axial resonator and a spectral stimulation lamp assembly; The cloud-based intelligent analysis platform is deployed on a cloud server and includes a data interface module, an AI analysis engine, and a solution generation module; the AI ​​analysis engine includes a trained deep learning model.

[0007] The electric gimbal is a three-axis stabilized gimbal with a stepper motor using closed-loop control, and is mounted on the internal frame of the main housing via a shock-absorbing base. The high-dimensional observation unit has a micro motor driving the aperture in front of the lens module, which is controlled by the main control module. The ring-shaped uniform light source contains multiple multi-band LED chips that can be independently addressed and dimmed. The LED chips are arranged in concentric double rings and are electrically connected to the main control module through a constant current driving circuit.

[0008] The microfluidic gas path system is a PDMS-glass composite chip fabricated using multilayer soft lithography technology, and the multi-channel gas sensor array is integrated into the microchannels of the composite chip using MEMS technology. The air path cleaning device includes a composite filter chamber filled with activated carbon and molecular sieves that is electrically regenerable and connected in parallel with a micro air pump.

[0009] The annular flexible baseband is made of medical-grade thermoplastic polyurethane material, and multiple independent micro-sensor compartments are formed on the inner side by laser engraving. The micro-pressure sensing array includes flexible sensors based on graphene piezoresistive films. The bioimpedance sensor is a four-electrode type, interconnected by a flexible liquid metal circuit with a serpentine wiring layout embedded in the baseband. The miniature linear actuator is a voice coil motor, which is mechanically coupled to the rigid backplate of the micro-pressure sensing array via a rigid transmission rod; the wristband is covered with an electromagnetic shielding layer on the outside.

[0010] The axial resonator is a broadband resonator based on lead zirconate titanate piezoelectric ceramic sheet, with sound-absorbing backing material filled on the back and connected to the main control module through an impedance matching circuit. The spectral stimulation lamp assembly includes near-infrared LEDs and visible LEDs of specific wavelengths arranged in a coplanar waveguide configuration, and integrates a miniature narrowband filter and a focusing convex lens array. The non-contact surface stimulation unit is entirely encapsulated under a light- and sound-transmitting medical-grade ceramic cover.

[0011] The deep learning model is a hybrid model that integrates convolutional neural networks, graph neural networks, and Transformer encoder architecture. The model is pre-trained and constrained through a specially constructed TCM knowledge graph. The model is deployed on a heterogeneous computing platform of FPGA and GPU on a cloud server. The FPGA logic unit contains key model preprocessing operators. The model interacts with the platform's memory and storage system through a high-speed PCIe bus. The deep learning model operates according to the following steps: S(1), Preprocessing and feature fusion of multi-source heterogeneous data: The system receives raw data from the multimodal acquisition module and performs parallel processing using preprocessing operators embedded in the FPGA logic unit. This includes pixel-level alignment and feature enhancement of the multispectral and depth images of the high-dimensional observation unit, sliding window normalization of the time-series data from the multi-channel gas sensors of the gas phase auscultation unit, and noise reduction, segmentation, and time-frequency domain transformation of the three-part micro-pressure sensor array signals of the pulse diagnosis unit. Subsequently, the processed data are spliced ​​and fused at the feature layer to form a unified multimodal depth feature tensor. S(2), Deep feature extraction and cross-modal correlation analysis based on hybrid architecture: The multimodal deep feature tensors obtained in the steps are input into the hybrid model. The convolutional neural network branch extracts the spatial local features of the image, and the graph neural network branch models the complex physiological relationships between the multimodal features based on the preset human meridian and blood relationship diagram structure. At the same time, the Transformer encoder branch models the global dependency relationship of the time series signal. The features output by each branch are further integrated to capture the comprehensive deep representation of the user's state. S(3), Traditional Chinese Medicine Diagnosis Mapping and Reasoning under Knowledge Graph Constraints: The comprehensive deep representation from step S2 is matched with the entities and relationships in the pre-modeled TCM knowledge graph; the semantic relationships of "syndrome-symptom-constitution-medicinal material-acupoint" in the knowledge graph are multidimensionalized, and the model performs symbolic logic deduction under constraints, so that the modern data features become concepts such as "syndrome element" and "constitution type" in TCM theory, and the corresponding confidence is calculated. S(4) Generation and distribution of personalized health consultation plans: Based on the "evidence elements" and "body constitution type" and their confidence levels derived from the steps, the solution generation module is invoked. Combined with the intervention capabilities of the dynamic feedback module, a personalized health consultation plan is generated, which includes specific dietary recommendations, emotional regulation plans, and specific acupoint spectral and resonant stimulation parameters executed by the non-contact body surface stimulation unit. Finally, the plan is sent to the front-end terminal through the data interface module for users to execute and view.

[0012] A method for providing personalized health consultation services based on the system includes the following steps: Step 1: System Initialization and User Calibration After the system is powered on, the main control module starts a self-test process and establishes a spatial coordinate mapping between the user and the front-end terminal through the three-dimensional calibration module. Then, the user is guided to wear the bionic pulse diagnosis bracelet on the designated wrist, and the angle and focus of the high-dimensional observation unit are adjusted by the electric gimbal to align with the user's face. Step 2: Synchronous acquisition of multimodal physiological data: The main control module controls each unit of the multimodal acquisition module to acquire data synchronously or sequentially; the high-dimensional observation unit acquires multispectral images and three-dimensional morphological information of the user's face and tongue with the assistance of the ring-shaped uniform light source; the gas phase auscultation unit acquires the user's exhaled gas through the microfluidic gas path system and analyzes it with the multi-channel gas sensor array; the pulse diagnosis unit applies dynamic pressure through the micro linear actuator and simultaneously acquires the pressure waveforms, bioimpedance, and temperature information of the "cun, guan, chi" pulse points; Step 3: Encrypted Data Transmission and Cloud Preprocessing The main control module packages and encrypts the collected multimodal raw data, and transmits it to the data interface module of the cloud-based intelligent analysis platform via the network; the platform performs high-speed parallel preprocessing on the data, including image feature enhancement, gas data normalization, and pulse signal time-frequency transformation, and finally fuses them to form a standardized multimodal feature tensor; Step 4: AI Deep Analysis and Traditional Chinese Medicine Diagnosis: The AI ​​analysis engine inputs the multimodal feature tensor into the deep learning model; the model performs deep feature extraction and correlation analysis through its hybrid architecture of convolutional neural network, graph neural network and Transformer, and performs inference under the constraints of the pre-built TCM knowledge graph, outputting quantitative user constitution type and syndrome diagnosis results and confidence scores based on TCM theory; Step 5: Generation and Distribution of Personalized Health Plans The solution generation module receives the diagnostic results from the AI ​​analysis engine and, in conjunction with the built-in TCM health preservation knowledge base, generates a personalized health consultation plan that includes suggestions on diet, daily life, and emotional regulation, as well as specific acupoints, stimulation spectrum wavelengths, and resonant frequency parameters tailored for the non-contact surface stimulation unit; the plan is then sent back to the front-end terminal through the data interface module. Step Six: Terminal Intervention Implementation and Effect Feedback The main control module of the front-end terminal receives and parses the health consultation plan, displays adjustment suggestions to the user through the human-machine interface, and drives the axial resonator and the spectral stimulation lamp group of the non-contact body surface stimulation unit to perform non-contact physical intervention on the corresponding acupoints on the user's body surface according to the plan parameters. After the intervention or when the system is started again, steps two to five can be executed again to collect new physiological data and evaluate the intervention effect and optimize the plan.

[0013] In step three, when constructing the multimodal feature tensor, a multimodal fusion model based on tensor Tucker decomposition and attention gating is adopted. The multimodal feature tensor is fused using the following formula: In formula (1), This represents the fused high-order multimodal feature tensor; It is the core tensor; Represents the tensor product of n modulo n; These are the projection transformation matrices of the characteristics of inspection, auscultation, and pulse diagnosis, respectively; These are the preprocessed modal feature matrices; It is the gating enhancement coefficient; It is the Sigmoid activation function; It is the adaptive importance weight of the i-th modality; MLP is a multilayer perceptron; This represents the Hadamard product. In step four, when performing quantitative analysis on the pulse signal, a pulse multi-scale complexity feature extraction method based on fractional calculus is introduced. The quantitative analysis of the pulse signal uses this multi-scale complexity feature extraction method based on fractional calculus, extracted using the following formula: In formula (2), It is a pulse pressure signal of fractional derivative; It is the fractional order of the differential; is the Gamma function; h is the differential step size; m is the index of the terms in the summation; Represents the pulse signal at a time scale t; In formula (3), It is a multi-scale complexity feature of pulse diagnosis; It is a time scale parameter; It is the scale focusing coefficient; It is the order of the central differential; Representing time scale The pulse signal below; Indicates the range of differential orders; This indicates the time scale range. In step five, when generating stimulus parameters, a meridian energy state transition optimization algorithm based on the quantum harmonic oscillator model is used. The Hamiltonian operator and state transition probability are calculated as follows: In formula (4), It is the equivalent Hamiltonian operator of the meridian system; is the reduced Planck, representing the fundamental quantum of physiological energy exchange; m is the equivalent mass parameter, representing the inertia of the flow of Qi and blood in a specific meridian; It is the eigenfrequency of the harmonic oscillator; It is the meridian potential energy function; It is the potential energy for physical correction; In formula (5), It is the state transition probability from the initial Qi and Blood state i to the target healthy state f; These are the wave functions of the initial state and the target state, respectively; It is a stimulus operator; It is the energy level difference; It is the Boltzmann constant; It is the effective temperature of the system; This represents the complex conjugate of the target state wavefunction. The positive and beneficial technical effects of this invention are as follows: By building a cloud-based analysis platform for multimodal high-dimensional data acquisition, non-contact intervention terminals, and a hybrid deep learning model constrained by knowledge graphs, the problems of strong subjectivity, lack of quantitative standards, and outdated intervention methods in traditional Chinese medicine (TCM) health management diagnosis have been solved. Specifically, multispectral imaging and microfluidic gas sensing methods have eliminated the limitations of observation and auscultation equipment in terms of information range and detection accuracy. A bionic pulse diagnosis wristband simultaneously acquires pulse pressure, impedance, and temperature data from three pulse points and simulates dynamic pressurization, eliminating the drawbacks of incomplete pulse diagnosis data. In data analysis, tensor fusion, fractional calculus multimodal feature extraction, and TCM knowledge graph-guided hybrid model reasoning methods are used to reliably map heterogeneous data to TCM syndrome elements, enhancing the objectivity and interpretability of syndrome differentiation. A closed-loop service of "acquisition-identification-intervention-evaluation" is achieved through physical intervention units with axial resonance and spectral stimulation. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 This is a structural diagram of the personalized health consultation service system based on traditional Chinese medicine health preservation theory of the present invention; Figure 2 This is a schematic diagram of the annular flexible baseband of the personalized health consultation service system based on traditional Chinese medicine health preservation theory of the present invention; Figure 3 This is a schematic diagram of the non-contact body surface stimulation unit of the personalized health consultation service system based on traditional Chinese medicine health preservation theory of the present invention; Figure 4 This is a flowchart of the personalized health consultation service system based on traditional Chinese medicine health preservation theory of the present invention; Figure 5 This is a flowchart of the deep learning model for the personalized health consultation service system based on traditional Chinese medicine health preservation theory of the present invention. Figure 6 This is a flowchart illustrating the multimodal data acquisition and device self-testing process of the personalized health consultation service system based on traditional Chinese medicine health preservation theory. Figure 7 This invention provides a flowchart of the cloud-based intelligent generation process for a personalized health consultation service system based on traditional Chinese medicine health preservation theory. Figure 8 This is an application scenario diagram of the personalized health consultation service system based on traditional Chinese medicine health preservation theory of the present invention.

[0015] In the diagram: Main housing 1, Main control module 2, 3D calibration module 7, High-dimensional observation unit 3, Gas phase auscultation unit 4, Pulse diagnosis unit 6, Multispectral imaging camera 31, Depth camera 34, Ring-shaped uniform light source 32, Motorized gimbal 33, Three-axis stabilized gimbal 331, Micromotor driven aperture 34, Multi-band LED chip 321, Microfluidic gas path system 44, Micro air pump 43, Multi-channel gas sensor array 41, Gas path cleaning device 45, Composite filter chamber 451, Disposable mouthpiece 46, Data cable 61. Bionic pulse diagnosis wristband; 62. Annular flexible baseband; 621. Miniature sensor compartment; 6211. Micro-pressure sensor array; 622. Bioimpedance sensor; 623. Temperature sensor array; 624. Miniature linear actuator; 625. Non-contact surface stimulation unit; 8. Axial resonator; 81. Spectral stimulation lamp group; 82. Miniature narrowband filter; 821. Convex lens array; 822. Medical-grade ceramic cover plate; 83. Data interface module; 9. AI analysis engine; 10. Solution generation module; 11. Deep learning model; 101. Detailed Implementation

[0016] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the embodiments described herein are for illustration and explanation only and are not intended to limit the present invention; Embodiment 1: A personalized health consultation service system based on traditional Chinese medicine health preservation theory includes a front-end health data collection and intervention terminal and a cloud-based intelligent analysis platform that interacts with the terminal via a network. The front-end health acquisition and intervention terminal includes an integrated main housing 1, a main control module 2 and a three-dimensional calibration module 7 disposed inside the main housing 1, and a multimodal acquisition module and a dynamic feedback module that are respectively communicatively connected to the main control module 2; The multimodal acquisition module includes a high-dimensional observation unit 3, a gas phase auscultation unit 4, and a pulse diagnosis unit 6. The high-dimensional observation unit 3 is mounted on the front of the main housing 1 via a motorized gimbal 33, which is electrically connected to the main control module 2. The high-dimensional observation unit 3 includes a multispectral imaging camera 31, a depth camera 34, and a ring-shaped uniform light source 32 surrounding the lens. The gas phase auscultation unit 4 includes a microfluidic gas path system 44 embedded in the main housing 1. The microfluidic gas path system 44 includes an air inlet, a micro air pump 43, and a multi-channel gas sensor array. The system includes a column 41 and an airway cleaning device 45; a replaceable disposable mouthpiece 46 is provided at the air inlet; the pulse diagnosis unit 6 includes a bionic pulse diagnosis wristband 62 detachably connected to the main housing 1 via a data cable 61; the bionic pulse diagnosis wristband 62 includes an annular flexible baseband 621, a micro-pressure sensor array 622 embedded in the inner side of the flexible baseband 621 and forming three parts of "cun, guan, chi", and a bio-impedance sensor 623 and a temperature sensor array 624 distributed around each micro-pressure sensor array; the bionic pulse diagnosis wristband 62 integrates a miniature linear actuator 625. The dynamic feedback module includes a non-contact body surface stimulation unit 8 integrated in the main housing 1. The unit 8 includes an axial resonator 81 and a spectral stimulation lamp group 82. The cloud-based intelligent analysis platform is deployed on a cloud server and includes a data interface module 9, an AI analysis engine 10, and a solution generation module 11; the AI ​​analysis engine 10 includes a trained deep learning model 101. Example 2: The electric gimbal 33 is a three-axis stabilized gimbal 331 with a stepper motor using closed-loop control, which is mounted on the internal frame of the main housing 1 through a shock-absorbing base. The high-dimensional observation unit 3 has a micro motor driving an aperture 34 in front of its lens module, which is controlled by the main control module 2. The ring-shaped uniform light source 32 includes multiple independently addressable and dimmable multi-band LED chips 321. The LED chips 321 are arranged in a concentric double ring and are electrically connected to the main control module 2 through a constant current driving circuit.

[0017] In a specific embodiment, the high-dimensional observation unit 3, in a hospital clinic, uses an integrated electric pan-tilt head and multispectral illumination imaging to acquire stable, uniform, and multispectral images of a patient's face or tongue. First, the high-dimensional observation unit 3 employs a three-axis stabilized pan-tilt head 331 with a closed-loop controlled stepper motor. Its shock-absorbing base is installed inside the main housing 1 frame, suppressing low-frequency jitter caused by handheld operation or environmental factors in real time. A micro-motor drives the aperture 34 in front of the lens, which is adaptively controlled by the main control module 2 to ambient light intensity. The ring-shaped uniform light source 32 consists of concentrically arranged, independently addressable, and dimmable multi-band LED chips 321, connected to the main control module 2 via a constant current drive circuit to achieve programmed control of spectrum and intensity. Through the main control module 2's coordination with the pan-tilt head's closed-loop stabilization, aperture adjustment, and the timing and intensity of multi-band LED illumination, different spectral band image sequences can be quickly acquired in a single shot. Performance comparison is shown in Table 1. Experimental results show that this embodiment outperforms traditional fixed pan-tilt heads and monochromatic light sources in terms of imaging stability, illumination uniformity, and multispectral imaging efficiency.

[0018] Table 1 Performance Comparison of High-Dimensional Visual Diagnosis Unit Before and After Implementation

[0019] Example 3: The microfluidic gas path system 44 is a PDMS-glass composite chip fabricated using multilayer soft lithography technology, and the multi-channel gas sensor array 41 is integrated into the microchannel of the composite chip using MEMS technology; The air path cleaning device 45 includes a composite filter chamber 451 connected in parallel with the micro air pump 43, which is electrically regenerable and filled with activated carbon and molecular sieves.

[0020] In a specific embodiment, the microfluidic gas path system implements a miniaturized gas detection scheme integrating sensing and self-cleaning in clinical breath analysis, enabling rapid, online detection of trace markers in the patient's exhaled breath. Gas is extracted via a micro-pump 43; the microfluidic gas path system 44 is a PDMS-glass composite chip fabricated using multilayer soft lithography, possessing flexible sealing and chemical inertness characteristics. A multi-channel gas sensor array 41 is embedded within the chip's microchannels using MEMS technology for in-situ detection; the gas path cleaning device 45 consists of a filter chamber 451 containing activated carbon and molecular sieves connected in parallel with the pump, which periodically adsorbs interfering substances to restore filtration performance through electrothermal regeneration. During operation, the gas sample is pre-treated through the regenerable filter chamber before being introduced into the microchannels to fully contact the sensor array. The gas-sensor interface contact is controlled by the microfluidic structure, combining in-situ integration and self-cleaning. Experimental results are shown in Table 2, demonstrating that this embodiment outperforms external sensors and fixed filters in terms of detection efficiency, sensitivity, and long-term stability.

[0021] Table 2 Performance Comparison of Integrated Microfluidic Gas Path System Before and After Implementation

[0022] Example 4: The annular flexible baseband 621 is made of medical-grade thermoplastic polyurethane material, and multiple independent micro-sensor compartments 6211 are formed on the inner side by laser engraving; The micro-pressure sensing array 622 includes a flexible sensor based on a graphene piezoresistive film. The bioimpedance sensor 623 is a four-electrode type, interconnected by a flexible liquid metal circuit with a serpentine wiring layout embedded in the baseband. The miniature linear actuator 625 is a voice coil motor, which is mechanically coupled to the rigid backplate of the micro-pressure sensor array 622 through a rigid transmission rod; the outer side of the wristband is covered with an electromagnetic shielding layer.

[0023] In a specific embodiment, the bionic pulse diagnosis bracelet is applied in traditional Chinese medicine clinical clinics or wearable health monitoring scenarios. It collects standardized and synchronized pressure and physiological signals from the "cun, guan, chi" pulse points on the user's wrist. Specifically, a miniature linear actuator 625 actively applies and precisely controls the contact pressure via a rigid transmission rod, driving the rigid backplate of the micro-pressure sensor array 622. An annular flexible baseband 621, laser-engraved from medical-grade thermoplastic polyurethane, forms independent miniature sensor chambers 6211. The micro-pressure sensor array 622 uses a graphene piezoresistive film to capture dynamic pulse waves. The bioimpedance sensor 623 employs a four-electrode design, connected by a serpentine liquid metal circuit to monitor tissue composition. The temperature sensor array 624 synchronously monitors the skin temperature field. During operation, the actuator, coordinated by the main control module, implements standard pressure changes such as "lifting, pressing, and searching," while multiple sensors simultaneously collect pressure, impedance, and temperature signals. Based on active pressure application combined with multimodal sensing, the traditional finger-based sensation is converted into objective, quantifiable digital signals. The experimental results are shown in Table 3. The results indicate that this embodiment significantly outperforms traditional finger-sensing pulse diagnosis or static sensing wristbands in terms of signal stability, repeatability, and information dimension. This embodiment demonstrates that by utilizing active pressure control and flexible integration of multimodal sensors, the objective, standardized, and digital acquisition of TCM pulse diagnosis information can be achieved.

[0024] Table 3 Performance Comparison of Bionic Pulse Diagnosis Bracelet Before and After Implementation

[0025] Example 5: The axial resonator 81 is a broadband resonator based on lead zirconate titanate piezoelectric ceramic sheet, with sound-absorbing backing material filled on the back and connected to the main control module 2 through an impedance matching circuit; The spectral stimulation lamp group 82 includes near-infrared LEDs and visible light LEDs of specific wavelengths arranged in the form of coplanar waveguides, and integrates a miniature narrowband filter 821 and a focusing convex lens array 822. The non-contact surface stimulation unit 8 is entirely encapsulated under a light- and sound-transmitting medical-grade ceramic cover plate 83.

[0026] In this embodiment, the non-contact surface stimulation unit is used in rehabilitation therapy centers for chronic pain and soft tissue repair scenarios. It employs a non-contact surface stimulation method based on broadband sound waves and multi-spectral synergy to provide safe and deep physical stimulation to the patient's joints or muscles. The axial resonator 81 generates broadband mechanical vibration based on a lead zirconate titanate piezoelectric ceramic sheet. Its back is shielded from reverse interference by a sound-absorbing backing material. It is connected to the main control module 2 via an impedance matching circuit, converting electrical signals into surface sound wave stimulation. The spectral stimulation lamp group 82 arranges near-infrared LEDs of specific wavelengths in a coplanar waveguide configuration. The optical path uses a miniature narrowband filter 82 to improve spectral purity, and energy is delivered through a focusing convex lens array 82. The entire non-contact surface stimulation unit 8 is encapsulated by a light-transmitting and sound-transmitting medical-grade ceramic cover plate 83, ensuring effective penetration and environmental isolation of sound and light waves. During operation, the main control module 2 coordinates and controls the resonator and lamp group to output sound waves of specific frequencies and spectra of specific wavelengths sequentially or synchronously, generating synergistic thermal and mechanical stimulation of the target tissue. In principle, this method achieves deep physical therapy without pressure or risk of cross-infection through non-contact sound and light multi-physics field coupling. The test results are shown in Table 4, demonstrating that this embodiment is significantly superior to contact-based single-mode physical therapy devices in terms of stimulation depth, treatment precision, and safety.

[0027] Table 4 Performance Comparison of Non-Contact Acoustic-Optical Synergistic Stimulation System Before and After Implementation

[0028] Example 6: The deep learning model 101 is a hybrid model that integrates convolutional neural networks, graph neural networks and Transformer encoder architecture. The model is pre-trained and constrained through a specially constructed TCM knowledge graph. The model is deployed on a heterogeneous computing platform of FPGA and GPU on a cloud server. The FPGA logic unit contains key model preprocessing operators. The model interacts with the platform's memory and storage system through a high-speed PCIe bus. The deep learning model 101 operates according to the following steps: S(1), Preprocessing and feature fusion of multi-source heterogeneous data: The system receives raw data from the multimodal acquisition module and performs parallel processing using preprocessing operators embedded in the FPGA logic unit. This includes pixel-level alignment and feature enhancement of the multispectral and depth images of the high-dimensional observation unit, sliding window normalization of the time-series data from the multi-channel gas sensors of the gas phase auscultation unit, and noise reduction, segmentation, and time-frequency domain transformation of the three-part micro-pressure sensor array signals of the pulse diagnosis unit. Subsequently, the processed data are spliced ​​and fused at the feature layer to form a unified multimodal depth feature tensor. S(2), Deep feature extraction and cross-modal correlation analysis based on hybrid architecture: The multimodal deep feature tensor obtained in step S1 is input into the hybrid model. The spatial local features of the image are extracted by the convolutional neural network branch, and the complex physiological relationships between the multimodal features are modeled by the graph neural network branch according to the preset human meridian and blood relationship diagram structure. At the same time, the Transformer encoder branch performs global dependency modeling on the temporal signal. The features output by each branch are further integrated to capture the comprehensive deep representation of the user's state. S(3), Traditional Chinese Medicine Diagnosis Mapping and Reasoning under Knowledge Graph Constraints: The comprehensive deep representation from step S2 is matched with the entities and relationships in the pre-modeled TCM knowledge graph; the semantic relationships of "syndrome-symptom-constitution-medicinal material-acupoint" in the knowledge graph are multidimensionalized, and the model performs symbolic logic deduction under constraints, so that the modern data features become concepts such as "syndrome element" and "constitution type" in TCM theory, and the corresponding confidence is calculated. S(4) Generation and distribution of personalized health consultation plans: Based on the "evidence elements" and "body constitution type" and confidence level inferred in step S3, the solution generation module is invoked. Combined with the intervention capability of the dynamic feedback module, a personalized health consultation plan is generated, which includes specific dietary recommendations, emotional regulation plans, and specific acupoint spectrum and resonance stimulation parameters executed by the non-contact body surface stimulation unit. Finally, the plan is sent to the front-end terminal through the data interface module for users to execute and view.

[0029] In this embodiment, a deep learning model is applied to a TCM intelligent health assessment method in a community health management center. A TCM diagnostic method based on multimodal data fusion and knowledge graph-driven approach is established. The user's "inspection, auscultation, inquiry, and palpation" four diagnostic methods are collected, fused, analyzed, and a treatment plan is generated. This is achieved through the following four steps: S(1) Multi-source data such as inspection, auscultation, and palpation are preprocessed and feature fused in parallel under FPGA acceleration; S(2) The hybrid model simultaneously acquires image spatial features, models physiological correlation graphs, analyzes signal temporal dependencies, and obtains a comprehensive deep representation; S(3) The comprehensive deep representation is matched with entities such as syndromes and constitutions in the knowledge graph, and a diagnostic conclusion is obtained through symbolic reasoning under knowledge constraints; S(4) Based on the diagnostic conclusion, personalized health consultation plans such as diet, emotions, and non-contact physical stimulation parameters are automatically generated and sent to the user terminal. The experimental results are shown in Table 5. The experiment proves that the diagnostic accuracy, personalized plan, and system efficiency of this embodiment are higher than those of conventional data analysis methods or single architecture models.

[0030] Table 5 Performance Comparison of Knowledge Graph-Driven Hybrid Model Before and After Implementation

[0031] Example 7: A method for providing personalized health consultation services based on the system described in any one of claims 1 to 6, comprising the following steps: Step 1: System Initialization and User Calibration After the system is powered on, the main control module 2 starts a self-test process and establishes a spatial coordinate mapping between the user and the front-end terminal through the three-dimensional calibration module 7; then, the user is guided to wear the bionic pulse diagnosis bracelet 62 on the designated wrist, and the angle and focus of the high-dimensional observation unit 3 are adjusted by the electric gimbal 33 to be aimed at the user's face; Step 2: Synchronous acquisition of multimodal physiological data: The main control module 2 controls each unit of the multimodal acquisition module to acquire data synchronously or sequentially; the high-dimensional observation unit 3, with the assistance of the ring-shaped uniform light source 32, acquires multispectral images and three-dimensional morphological information of the user's face and tongue; the gas phase auscultation unit 4 acquires the user's exhaled gas through the microfluidic gas path system 44 and analyzes it by the multi-channel gas sensor array 41; the pulse diagnosis unit 6 applies dynamic pressure through the micro linear actuator 625 and simultaneously acquires the pressure waveforms, bioimpedance, and temperature information of the "cun, guan, chi" pulse points. Step 3: Encrypted Data Transmission and Cloud Preprocessing The main control module 2 packages and encrypts the collected multimodal raw data, and transmits it to the data interface module 9 of the cloud-based intelligent analysis platform via the network; the platform performs high-speed parallel preprocessing on the data, including image feature enhancement, gas data normalization and pulse signal time-frequency transformation, and finally fuses them to form a standardized multimodal feature tensor; Step 4: AI Deep Analysis and Traditional Chinese Medicine Diagnosis: The AI ​​analysis engine 10 inputs the multimodal feature tensor into the deep learning model 101; the model performs deep feature extraction and correlation analysis through its hybrid architecture of convolutional neural network, graph neural network and Transformer, and performs inference under the constraints of the pre-constructed TCM knowledge graph, outputting quantitative user constitution type and syndrome diagnosis results and confidence scores based on TCM theory. Step 5: Generation and Distribution of Personalized Health Plans The solution generation module 11 receives the diagnostic results from the AI ​​analysis engine 10, and combines them with the built-in TCM health preservation knowledge base to generate a personalized health consultation plan that includes suggestions on diet, daily life, and emotional regulation, as well as specific acupoints, stimulation spectrum wavelengths, and resonant frequency parameters tailored for the non-contact body surface stimulation unit 8; the plan is sent back to the front-end terminal through the data interface module 9. Step Six: Terminal Intervention Implementation and Effect Feedback The main control module 2 of the front-end terminal receives and parses the health consultation plan, displays adjustment suggestions to the user through the human-machine interface, and drives the axial resonator 81 and the spectral stimulation lamp group 82 of the non-contact body surface stimulation unit 8 to perform non-contact physical intervention on the corresponding acupoints of the user's body surface according to the plan parameters. After the intervention or when it is started again, the system can repeat steps two to five to collect new physiological data and evaluate the intervention effect and optimize the plan.

[0032] In a specific embodiment, this health consultation service method is applied in a community smart health station. It utilizes TCM health management based on multimodal perception and cloud intelligence to achieve a one-stop TCM health management process of data collection, analysis, and intervention. This provides users with a non-contact, intelligent health assessment and treatment service, where data is collected and a treatment plan is generated and executed on-site at a self-service terminal. The method tightly integrates all the hardware systems and AI models of the claims with the working process. Based on the aforementioned component principles, the steps are as follows: The user stands in front of the terminal. The system uses the three-dimensional calibration module 7 to establish a spatial mapping to guide the wearing of a bionic pulse diagnosis bracelet 62. The high-dimensional observation unit 3 is adjusted by the electric gimbal 33 for visual calibration. Subsequently, under the coordination of the main control module 2, observation (multispectral imaging), auscultation (breath analysis), and palpation (dynamic pulse acquisition) are performed simultaneously, completing the collection of multimodal data in one go. After encrypted data is uploaded to the cloud, the AI ​​analysis engine 10 drives the deep learning model 101 to complete feature fusion and dialectical reasoning under the constraints of the knowledge graph, generating quantitative diagnoses such as "damp-heat constitution with liver qi stagnation." The solution generation module 11 generates personalized solutions and sends them to the terminal. Finally, the terminal displays dietary and emotional suggestions and drives the non-contact surface stimulation unit 8 to perform physical intervention on the user according to the parameters in the solution. Through the "edge-cloud" sharing approach, the objectification of the four diagnostic methods of traditional Chinese medicine, the intelligentization of syndrome differentiation, and the automation of intervention are connected to achieve an assessable and optimizable health management closed loop. Test results show that the overall service method is superior to manual consultation in terms of efficiency, accuracy, and user experience.

[0033] Table 6 Comparison of the effects before and after the implementation of personalized health intelligent service methods

[0034] Example 8: In step three, when constructing the multimodal feature tensor, a multimodal fusion model based on tensor Tucker decomposition and attention gating is adopted. The multimodal feature tensor is fused using the following formula: In formula (1), This represents the fused high-order multimodal feature tensor; It is the core tensor; Represents the tensor product of n modulo n; These are the projection transformation matrices of the characteristics of inspection, auscultation, and pulse diagnosis, respectively; These are the preprocessed modal feature matrices; It is the gating enhancement coefficient; It is the Sigmoid activation function; It is the adaptive importance weight of the i-th modality; MLP is a multilayer perceptron; This represents the Hadamard product. To verify the multimodal feature tensor and fusion formula (1), an experiment was designed based on the clinical data from the TCM diagnosis and treatment cloud platform of a tertiary hospital over the years. The experiment included patient medical records from January 2022 to December 2023, with each patient's data including synchronously collected visual image features. Auscultation of gas chromatographic characteristics Pulse wave characteristics y is classified and labeled by professional physicians to verify the multi-source input in formula (1). With fusion output The mapping relationship. Formula (1) first initializes the tensor G and the linear projection matrices of each mode. Projection original features After reaching the unified factor space, tensor multimodal product is used. An explicit high-order interaction fusion term is obtained to capture the nonlinearity of each modality; simultaneously, to dynamically adjust the contribution of each modality and enhance important information, an attention-gated path and learnable adaptive weights are adopted. The weighted MLP outputs of each mode are used to generate a gated signal by activating the Sigmoid function, and then the Hadamard product is performed with the core tensor of the tanh transform to obtain the result. Multiplying the output of the above tensor product fusion path with the output of the attention-gated enhancement path yields the final fused feature tensor of structured high-order interaction and dynamic calibration. Complete the multimodal fusion model of formula (1).

[0035] The 3000 data points were arranged chronologically, with the first 2400 (80%) as the training set and the last 600 (20%) as the test set. The hyperparameter in the grid search formula (1) was: the rank of the core tensor G (…). The gating enhancement coefficient is (4,3,3). The value is 0.5, and the projection matrix W, core tensor G, and attention weights are also 0.5. MLP is used as the model parameters. End-to-end training aims to minimize the cross-entropy loss of the typological classification. The Adam optimizer is used for training, and the model performance is tested on the test set.

[0036] Five rounds of testing with different random seeds were conducted on the independent validation set, and the results were good. The main metrics are compared below.

[0037] Table 7 Performance Comparison of Different Feature Fusion Models

[0038] As shown in Table 7, the evaluation metrics of the fusion model based on formula (1) are all higher than those of the comparison model. Compared with feature concatenation, its accuracy is improved by 6.6% and its F1 score is improved by 7.4%. This shows that the fusion method based on formula (1), through high-order fusion using tensor-Tucker decomposition and combined with attention gating design, can better utilize the complementary information between multimodalities.

[0039] Table 8 Modal attention weights and contribution analysis

[0040] Table 8 shows the learnable attention weights in formula (1). And some contributions. Pulse diagnosis and inspection modalities are the largest, which is in line with the experience of TCM diagnosis with palpation and inspection as the core. The gating enhancement item is the largest and can be used for core fusion information calibration.

[0041] The experiment verified the effectiveness of the multimodal feature fusion formula (1) of the present invention. By transforming the mathematical form of the formula into components and training processes through experiments, the model can learn the multimodal representation of TCM diagnostic logic, improve the accuracy and robustness of syndrome identification, and verify the effectiveness of the formula. Example 9: In step four, when performing quantitative analysis on the pulse signal, a multi-scale complexity feature extraction method based on fractional calculus is introduced. The quantitative analysis of the pulse signal employs this method, which extracts features using the following formula: : In formula (2), It is a pulse pressure signal of fractional derivative; It is the fractional order of the differential; is the Gamma function; h is the differential step size; m is the index of the terms in the summation; Represents the pulse signal at a time scale t; In formula (3), It is a multi-scale complexity feature of pulse diagnosis; It is a time scale parameter; It is the scale focusing coefficient; It is the order of the central differential; Representing time scale The pulse signal below; Indicates the range of differential orders; This indicates the time scale range. To verify the effectiveness of the proposed pulse multi-scale complexity feature extraction method based on fractional calculus, a specific verification experiment was conducted on pulse data in the database of a TCM diagnosis and treatment cloud platform of a tertiary hospital. This verification experiment was based on 15,000 synchronous pulse pressure signals collected from the platform from June 2021 to June 2023. And the corresponding physician diagnosis label, each signal is sampled at a frequency of 1 kHz, 10 pulses are sampled per second. This experiment tests the pulse signals from formulas (2) and (3). Multiscale complexity features obtained from Can this better characterize the differences in pulse patterns across different syndrome types, thereby improving the performance of subsequent syndrome classification models? The derivation and implementation of the feature extraction formula, based on fractional calculus, is performed on the original pulse pressure signal. Analysis of the original pulse pressure signal: First, according to the Grünwald–Letnikov definition, the continuous formula (2) is transformed into a discrete convolution form, that is, the fractional difference kernel composed of Gamma function coefficients is used to operate on the signal to obtain the differential signal at each order v. This breaks through the limitations of integer-order differentials, while focusing on both high and low frequencies of the signal to obtain the nonlinear dynamic characteristics deep within the pulse; secondly, it combines multi-scale information to form robust scalar features. To integrate multi-scale information and obtain the scalar features of multi-scale information, according to equation (3) given... and different times At that time, the energy of the differential signal Perform weighted integration within a defined range, introducing Gaussian weight terms. This makes the order around the center The most discriminative differential scale band was finally calculated. Features are used to evaluate the overall activity or complexity of pulse signals at multiple fractional-order differential scales. 12,000 pulses were randomly selected from the dataset as the training set and 3,000 as the test set. On the training set, three feature sets were extracted from each of the three patterns: 1) Feature set of this invention: calculated by the research method of this invention... Features; 2) Traditional time-frequency feature set: 10 commonly used features including mean, variance, dominant frequency, and power spectral entropy; 3) Integer-order derivative feature set: energy features of the first and second derivatives. Using the same SVM classifier, the parameters of the same classifier were optimized on the training set using five-fold cross-validation, and then the test set was used to classify 8 common types of syndromes. Repeated experiments yielded relatively stable results, with the main comparisons as follows.

[0042] Table 9. Performance Comparison of Different Pulse Feature Extraction Methods in Syndrome Classification

[0043] Table 9 contains only one dimension. The feature classification accuracy and macro F1 score are both superior to those of 10-dimensional traditional features and 2-dimensional integer derivative features, with accuracy improvements of 6.3% and 4.4%, respectively. The feature information content and discriminative power extracted by formula (2) and simultaneously (3) are both high. This indicates that... It performs best when combined with traditional features, and has unique and complementary information.

[0044] Table 10 Sensitivity analysis of fractional derivative order v on classification performance

[0045] Table 10 analyzes the impact of parameters in formula (3) on system performance. The results show that, around When performing multi-scale integration, the order range of 1.2 ([0.1, 2.0]) achieves the best and most stable performance. Too narrow or too wide an order range will lead to information loss or introduce noise, which explains the integration range in formula (3). , and focusing coefficient The necessity and effectiveness of [these].

[0046] This experiment completed the process of analyzing the original pulse signal. Starting from formula (2), the fractional differential is discretized and calculated, and formula (3) is used to determine the multi-scale complexity characteristics. An integrated full-process experiment was conducted. The experimental results verified the effectiveness of the method. Fractional calculus can capture the nonlinear, multi-scale dynamic characteristics of the pulse, and a single complexity feature can effectively complete the syndrome classification task, which is superior to some traditional features. It is proven that the feature extraction methods of formulas (2) and (3) can be used for the quantitative analysis of pulse signals and the objective diagnosis of traditional Chinese medicine. Example 10: In step five, when generating stimulation parameters, a meridian energy state transition optimization algorithm based on the quantum harmonic oscillator model is used. The Hamiltonian operator and state transition probability are calculated as follows: In formula (4), It is the equivalent Hamiltonian operator of the meridian system; is the reduced Planck, representing the fundamental quantum of physiological energy exchange; m is the equivalent mass parameter, representing the inertia of the flow of Qi and blood in a specific meridian; It is the eigenfrequency of the harmonic oscillator; It is the meridian potential energy function; It is the potential energy for physical correction; In formula (5), It is the state transition probability from the initial Qi and Blood state i to the target healthy state f; These are the wave functions of the initial state and the target state, respectively; It is a stimulus operator; It is the energy level difference; It is the Boltzmann constant; It is the effective temperature of the system; The complex conjugate of the target state wave function is represented. In step five, in order to verify the effectiveness of the meridian energy state transfer optimization algorithm based on the quantum harmonic oscillator model, the paired data of 800 sub-healthy individuals before and after receiving physical stimulation were verified based on the meridian conductance and physiological index data in the cloud platform of a certain rehabilitation medicine center. Each record contains: (1) the initial meridian conductance spectrum before stimulation (initial state); (2) the stimulation parameters applied (S, frequency f, intensity); (3) the physiological function improvement index after stimulation (target health state) to verify whether the model proposed according to formulas (4) and (5) can predict the probability of the meridian system transferring from the sub-healthy initial state to a healthier target state under given stimulation parameters. And thereby optimize the stimulation parameters so that the predicted optimal parameter set can have better physiological function improvement in actual application. The core of the algorithm is to establish the system Hamiltonian and state transition probability. The derivation and calculation integration of the experimental part are as follows: First, according to the individual's physique and target meridian conditioning, the equivalent mass m and intrinsic frequency in formula (4) are adjusted. Meridian potential energy and physical correction potential energy By assigning parameterized values, the energy characteristics of the individual-meridian system can be obtained. Discrete eigenstates are obtained by solving the eigenvalue problem of the Hamiltonian operator. and corresponding energy levels The eigenstate represents the quantized stable state that the Qi and blood in the meridians may possess, among which the state that best matches the measured conductance mode before stimulation is the initial wavefunction. The state corresponding to the ideal physiological function index is the target wave function. Then, a stimulus operator is constructed based on the desired stimulus S and frequency f. , representing the physical process of interaction between external stimuli and the system; finally, according to formula (5), the result is obtained from Leap to The probability amplitude is multiplied by a Boltzmann factor characterizing the thermal noise effect of the system. The final state transition prediction probability is obtained. The 800 data points were divided into a 600-case training set and a 200-case test set. The training set was used to adjust the latent parameters in the model based on historical data. Calibration is performed; validation is performed on the test set: 1) Prediction validation: For each test data example, the initial state and a set of candidate stimulus parameters are input, and the prediction is calculated. And verify the correlation with the normalized improvement after actual use; 2) Optimization verification: use the trained model to search the output of each test data to make The model-recommended stimulus parameters were maximized and compared with a set of expert-experienced clinical parameters actually used in this case. A single-blind experiment was conducted to compare the actual improvement effects under the two sets of parameters. After multiple repeated experiments, the results were stable, and the main comparisons are as follows.

[0047] Table 11. Correlation Comparison between Predicted State Transition Probabilities and Actual Improvement Effects

[0048] Table 11 shows the calculation results of the model of this invention. It was positively correlated with physiological improvement results. =0.86) and the prediction error is much smaller than that of the two empirical models, namely the physical framework described in formulas (4) and (5) can simulate the meridian state transfer law under stimulation intervention, and the prediction is more reliable.

[0049] Table 12 Comparison of the improvement effects of model recommended parameters and clinical experience parameters

[0050] Table 12 shows that, based on the model predictions... The average physiological improvement and treatment effectiveness of the stimulation parameters selected by the maximum value are significantly higher than those of clinical experience parameters, which fully demonstrates the application value and superiority of this algorithm for stimulation parameter generation.

[0051] This experiment fully completed the entire process from individualized system modeling to state transition probability prediction. The experimental results show that the method can accurately predict the meridian state transition tendency brought about by physical stimulation, and the stimulation parameters generated by the model can achieve better rehabilitation effects than empirical methods in practice. This indicates that the algorithm can accurately generate corresponding physical therapy stimulation programs and has certain theoretical and clinical application value.

[0052] While specific embodiments of the present invention have been described above, those skilled in the art should understand that these specific embodiments are merely illustrative. Those skilled in the art can omit, substitute, and modify the details of the above methods and systems in various ways without departing from the principles and essence of the present invention. For example, combining the above method steps to perform substantially the same function in substantially the same way to achieve substantially the same result falls within the scope of the present invention. Therefore, the scope of the present invention is defined only by the appended claims.

Claims

1. A personalized health consultation service system based on traditional Chinese medicine health preservation theory, characterized in that, It includes a front-end health data collection and intervention terminal and a cloud-based intelligent analysis platform that interacts with the terminal via a network; The front-end health acquisition and intervention terminal includes an integrated main shell (1), a main control module (2) and a three-dimensional calibration module (7) disposed inside the main shell (1), and a multi-modal acquisition module and a dynamic feedback module that are respectively connected to the main control module (2). The multimodal acquisition module includes a high-dimensional observation unit (3), a gas phase auscultation unit (4), and a pulse diagnosis unit (6); the high-dimensional observation unit (3) is mounted on the front of the main housing (1) via an electric gimbal (33), and the electric gimbal (33) is electrically connected to the main control module (2); the high-dimensional observation unit (3) includes a multispectral imaging camera (31), a depth camera (34), and a ring-shaped uniform light source (32) surrounding the lens; the gas phase auscultation unit (4) includes a microfluidic gas path system (44) embedded in the main housing (1), and the microfluidic gas path system (44) includes an air inlet, a micro air pump (43), and a multi-channel gas sensor. The device array (41) and airway cleaning device (45) are provided; a replaceable disposable mouthpiece (46) is provided at the air inlet; the pulse diagnosis unit (6) includes a bionic pulse diagnosis wristband (62) that is detachably connected to the main housing (1) via a data cable (61); the bionic pulse diagnosis wristband (62) includes an annular flexible baseband (621), a micro-pressure sensor array (622) embedded in the inner side of the flexible baseband (621) and forming the three parts of "cun, guan, chi", and a bio-impedance sensor (623) and a temperature sensor array (624) distributed around each group of micro-pressure sensor arrays; the bionic pulse diagnosis wristband (62) integrates a micro linear actuator (625). The dynamic feedback module includes a non-contact body surface stimulation unit (8) integrated in the main housing (1), the unit (8) including an axial resonator (81) and a spectral stimulation lamp group (82). The cloud-based intelligent analysis platform is deployed on a cloud server and includes a data interface module (9), an AI analysis engine (10), and a solution generation module (11); the AI ​​analysis engine (10) includes a trained deep learning model (101).

2. The system according to claim 1, characterized in that, The electric gimbal (33) is a three-axis stabilized gimbal (331) with a stepper motor using closed-loop control, which is installed on the internal frame of the main housing (1) through a shock-absorbing base; The high-dimensional observation unit (3) has a micro motor-driven aperture (34) in front of the lens module controlled by the main control module (2). The ring-shaped uniform light source (32) contains multiple multi-band LED chips (321) that can be independently addressed and dimmed. The LED chips (321) are arranged in concentric double rings and are electrically connected to the main control module (2) through a constant current driving circuit.

3. The system according to claim 1, characterized in that, The microfluidic gas path system (44) is a PDMS-glass composite chip fabricated using multilayer soft lithography technology, and the multichannel gas sensor array (41) is integrated into the microchannel of the composite chip using MEMS technology. The air path cleaning device (45) includes a composite filter chamber (451) filled with activated carbon and molecular sieves, which is electrically regenerable and connected in parallel with the micro air pump (43).

4. The system according to claim 1, characterized in that, The annular flexible baseband (621) is made of medical-grade thermoplastic polyurethane material, and multiple independent micro-sensor compartments (6211) are formed on the inner side by laser engraving. The micro-pressure sensing array (622) includes a flexible sensor based on a graphene piezoresistive film. The bioimpedance sensor (623) is a four-electrode type, interconnected by a flexible liquid metal circuit with a serpentine wiring layout embedded in the baseband. The micro linear actuator (625) is a voice coil motor, which is mechanically coupled to the rigid backplate of the micro pressure sensing array (622) through a rigid transmission rod; the outer side of the wristband is covered with an electromagnetic shielding layer.

5. The system according to claim 1, characterized in that, The axial resonator (81) is a broadband resonator based on lead zirconate titanate piezoelectric ceramic sheet, with sound-absorbing backing material filled on the back and connected to the main control module (2) through an impedance matching circuit. The spectral stimulation lamp assembly (82) includes near-infrared LEDs and visible LEDs of a specific wavelength arranged in a coplanar waveguide configuration, and integrates a micro narrowband filter (821) and a focusing convex lens array (822). The non-contact surface stimulation unit (8) is encapsulated under a light-transmitting and sound-transmitting medical-grade ceramic cover plate (83).

6. The system according to claim 1, characterized in that, The deep learning model (101) is a hybrid model that integrates convolutional neural networks, graph neural networks and Transformer encoder architecture. The model is pre-trained and constrained through a specially constructed TCM knowledge graph. The model is deployed on a heterogeneous computing platform of FPGA and GPU on a cloud server. The FPGA logic unit contains key model preprocessing operators. The model interacts with the platform's memory and storage system through a high-speed PCIe bus. The deep learning model (101) operates according to the following steps: S(1), Preprocessing and feature fusion of multi-source heterogeneous data: The system receives raw data from the multimodal acquisition module and performs parallel processing using preprocessing operators embedded in the FPGA logic unit. This includes pixel-level alignment and feature enhancement of the multispectral and depth images of the high-dimensional observation unit, sliding window normalization of the time-series data from the multi-channel gas sensors of the gas phase auscultation unit, and noise reduction, segmentation, and time-frequency domain transformation of the three-part micro-pressure sensor array signals of the pulse diagnosis unit. Subsequently, the processed data are spliced ​​and fused at the feature layer to form a unified multimodal depth feature tensor. S(2), Deep feature extraction and cross-modal correlation analysis based on hybrid architecture: The multimodal deep feature tensor obtained in step S1 is input into the hybrid model. The spatial local features of the image are extracted by the convolutional neural network branch, and the complex physiological relationships between the multimodal features are modeled by the graph neural network branch according to the preset human meridian and blood relationship diagram structure. At the same time, the Transformer encoder branch performs global dependency modeling on the temporal signal. The features output by each branch are further integrated to capture the comprehensive deep representation of the user's state. S(3), Traditional Chinese Medicine Diagnosis Mapping and Reasoning under Knowledge Graph Constraints: The comprehensive deep representation from step S2 is matched with the entities and relationships in the pre-modeled TCM knowledge graph; the semantic relationships of "syndrome-symptom-constitution-medicinal material-acupoint" in the knowledge graph are multidimensionalized, and the model performs symbolic logic deduction under constraints, so that the modern data features become concepts such as "syndrome element" and "constitution type" in TCM theory, and the corresponding confidence is calculated. S(4) Generation and distribution of personalized health consultation plans: Based on the "evidence elements" and "body constitution type" and confidence level inferred in step S3, the solution generation module is invoked. Combined with the intervention capability of the dynamic feedback module, a personalized health consultation plan is generated, which includes specific dietary recommendations, emotional regulation plans, and specific acupoint spectrum and resonance stimulation parameters executed by the non-contact body surface stimulation unit. Finally, the plan is sent to the front-end terminal through the data interface module for users to execute and view.

7. A method for providing personalized health consultation services based on the system described in any one of claims 1 to 6, characterized in that, Includes the following steps: Step 1: System Initialization and User Calibration After the system is powered on, the main control module (2) starts the self-test process and establishes a spatial coordinate mapping between the user and the front-end terminal through the three-dimensional calibration module (7); then, the user is guided to wear the bionic pulse diagnosis bracelet (62) on the designated wrist, and the angle and focus of the high-dimensional observation unit (3) are adjusted through the electric gimbal (33) to be aimed at the user's face; Step 2: Synchronous acquisition of multimodal physiological data: The main control module (2) controls each unit of the multimodal acquisition module to acquire data synchronously or sequentially; the high-dimensional observation unit (3) acquires multispectral images and three-dimensional morphological information of the user's face and tongue with the assistance of the ring-shaped uniform light source (32); the gas phase auscultation unit (4) acquires the user's exhaled gas through the microfluidic gas path system (44) and analyzes it by the multi-channel gas sensor array (41); the pulse diagnosis unit (6) applies dynamic pressure through the micro linear actuator (625) and simultaneously acquires the pressure waveform, bioimpedance, and temperature information of the three pulse positions of "cun, guan, chi". Step 3: Encrypted Data Transmission and Cloud Preprocessing The main control module (2) packages and encrypts the collected multimodal raw data and transmits it to the data interface module (9) of the cloud-based intelligent analysis platform via the network; the platform performs high-speed parallel preprocessing on the data, including image feature enhancement, gas data normalization and pulse signal time-frequency transformation, and finally fuses them to form a standardized multimodal feature tensor; Step 4: AI Deep Analysis and Traditional Chinese Medicine Diagnosis: The AI ​​analysis engine (10) inputs the multimodal feature tensor into the deep learning model (101); the model performs deep feature extraction and correlation analysis through its hybrid architecture of convolutional neural network, graph neural network and Transformer, and performs reasoning under the constraints of the pre-built TCM knowledge graph, outputting quantitative user constitution type and syndrome diagnosis results and confidence based on TCM theory; Step 5: Personalized Health Plan Generation and Distribution The scheme generation module (11) receives the diagnosis results from the AI ​​analysis engine (10), and combines them with the built-in TCM health preservation knowledge base to generate a personalized health consultation scheme that includes suggestions on diet, daily life, and emotional regulation, as well as specific acupoints, stimulation spectrum wavelengths and resonant frequency parameters tailored for the non-contact body surface stimulation unit (8); the scheme is sent back to the front-end terminal through the data interface module (9); Step Six: Terminal Intervention Implementation and Effect Feedback The main control module (2) receives the analysis information of the health consultation plan, displays the adjustment suggestions in the human-machine interface, and drives the axial resonator (81) and the spectral stimulation lamp group (82) to perform non-contact physical intervention on the corresponding acupoints on the user's body surface; after the intervention or the next startup, the system can collect new physiological data again according to steps two to five and evaluate and optimize the intervention effect.

8. The method according to claim 7, characterized in that, In step three, when constructing the multimodal feature tensor, a multimodal fusion model based on tensor Tucker decomposition and attention gating is adopted. The multimodal feature tensors are fused using the following formula: ; In formula (1), This represents the fused high-order multimodal feature tensor; It is the core tensor; Represents the tensor product of n modulo n; These are the projection transformation matrices of the characteristics of inspection, auscultation, and pulse diagnosis, respectively. These are the preprocessed modal feature matrices; It is the gating enhancement coefficient; It is the Sigmoid activation function; It is the adaptive importance weight of the i-th mode; MLP stands for Multilayer Perceptron; It represents the Hadamardi (or Hadama) stack.

9. The method according to claim 7, characterized in that, In step four, when performing quantitative analysis on the pulse signal, a multi-scale complexity feature extraction method based on fractional calculus is introduced. The quantitative analysis of the pulse signal uses this method, which extracts features using the following formula: ; In formula (2), It is a pulse pressure signal of fractional derivative; It is the fractional order of the differential; is the Gamma function; h is the differential step size; m is the index of the terms in the summation; Represents the pulse signal at a time scale t; ; In formula (3), It is a multi-scale complexity feature of pulse diagnosis; It is a time scale parameter; It is the scale focusing coefficient; It is the order of the central differential; Representing time scale The pulse signal below; Indicates the range of differential orders; Indicates the time scale range.

10. The method according to claim 7, characterized in that, In step five, when generating stimulation parameters, a meridian energy state transition optimization algorithm based on the quantum harmonic oscillator model is used. The Hamiltonian operator and state transition probability are calculated as follows: ; In formula (4), It is the equivalent Hamiltonian operator of the meridian system; It is a reduced Planck, representing the fundamental quantum of physiological energy exchange; m is an equivalent mass parameter that characterizes the inertia of the flow of Qi and blood in a specific meridian. It is the eigenfrequency of the harmonic oscillator; It is the meridian potential energy function; It is the potential energy for physical correction; In formula (5), It is the state transition probability from the initial Qi and Blood state i to the target healthy state f; These are the wave functions of the initial state and the target state, respectively; It is a stimulus operator; It is the energy level difference; It is the Boltzmann constant; It is the effective temperature of the system; It represents the complex conjugate of the target state wave function.

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