Medical artificial intelligence pulse diagnosis system based on photoelectric volume pulse wave and working method of medical artificial intelligence pulse diagnosis system

Abstract

The invention provides a photoelectric volume pulse wave-based medical artificial intelligence pulse diagnosis system and a working method thereof, and the system is characterized in that the system comprises a sensing module, a host module, a printing module and a cloud platform; the sensing module comprises a sensor front cover, a sensor rear cover, a sensor base, a photoelectric sensor module, a first processing unit, a first wireless communication unit, a first battery module, a lens and a touch panel; the host module comprises a machine body, a bottom cover, a display module, a voice interaction module, a second processing unit, a second wireless communication unit, a power switch, a power key and a decorative plate; the printing module comprises a printer upper cover, a printer lower cover, a third processing unit, a third wireless communication unit, a printing paper module, a printing head module and a second battery module; the cloud platform comprises a server, an artificial intelligence big data model and a traditional Chinese medicine standard pulse map database. The medical artificial intelligence pulse diagnosis system based on the photoelectric volume pulse waves and the working method of the medical artificial intelligence pulse diagnosis system have visual and intelligent diagnosis and treatment effects.

Description

Technical Field

[0001] The present invention relates to a medical artificial intelligence pulse diagnosis system based on photoplethysmography and a working method thereof, which is suitable for use in disease diagnosis in medical institutions such as traditional Chinese medicine departments, traditional Chinese medicine clinics, and traditional Chinese medicine hospitals, and belongs to the technical field of innovative medical device products. Background Art

[0002] Traditional Chinese Medicine (TCM) diagnostic methods primarily include the "Four Diagnosis": inspection, auscultation, questioning, and palpation. These four methods comprehensively analyze a patient's external appearance, physical signs, and symptoms to determine the nature, location, and severity of the disease, providing an accurate and effective basis for clinical diagnosis and treatment. Pulse diagnosis is a key diagnostic method within this "palpation" approach. Traditionally, a physician presses three fingers on the radial artery at the wrist, at the Cun, Guan, and Chi points, to sense the patient's heart rate, pulse strength, and other physical signs. This method, combined with physician experience, allows for analysis and diagnosis of the patient's condition. While this traditional method has theoretical support and practical effectiveness, it also suffers from subjective judgment errors and drawbacks, is difficult to digitize and visualize, and lacks artificial intelligence-assisted diagnosis. This has long been a challenge for both physicians and patients. Therefore, a digital, visual, and intelligent medical pulse diagnosis system is urgently needed to help physicians better analyze and diagnose patients' conditions.

[0003] Existing pulse diagnosis products use mechanical fingers to simulate three fingers pressing the radial artery in the patient's wrist. The fingertips are equipped with pressure sensors. This technology has problems such as low sensor accuracy, single parameters, and inaccurate simulation curves. It cannot meet the needs of diagnosis and treatment. Therefore, there is a need to develop more accurate technologies and products.

[0004] In summary, this field urgently needs an innovative medical pulse diagnosis system that is precise, digital, visual, and intelligent. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems of misunderstanding and deviation in the traditional finger sensing method in the existing Chinese medicine diagnosis and treatment technology.

[0006] Innovative photoelectric sensor technology is used to convert multiple parameters such as pulse and blood pressure into digital data and visualize them. Then, an artificial intelligence big data model is deployed on the cloud platform to compare standard pulse chart data to obtain accurate results and provide treatment recommendations. It can be used in medical institutions such as TCM departments, TCM clinics, and TCM hospitals.

[0007] In order to achieve the above objectives of the present invention, the present invention provides a medical artificial intelligence pulse diagnosis system based on photoplethysmography, which is characterized by:

[0008] The present invention comprises a sensor module, a host module, a printing module and a cloud platform. The sensor module comprises a sensor front cover, a sensor back cover, a sensor base, a photoelectric sensor module, a first processing unit, a first wireless communication unit, a first battery module, a lens and a touchpad. The sensor module, the first processing unit, the first wireless communication unit, the first battery module, the lens and the touchpad are all detachably arranged in the accommodating cavity formed by the sensor front cover and the sensor back cover. The host module comprises a body, a bottom cover, a display module, a voice interaction module, a second processing unit, a second wireless communication unit, a power switch, a power button and a decorative panel. The voice interaction module, the second processing unit, the second wireless communication unit and the power switch are all detachably arranged in the accommodating cavity formed by the body and the bottom cover. The printing module comprises a printer upper cover, a printer lower cover, a third processing unit, a third wireless communication unit, a printing paper module, a print head module and a second battery module. The third processing unit, the third wireless communication unit, the printing paper module, the print head module and the second battery module are all detachably arranged in the accommodating cavity formed by the printer upper cover and the printer lower cover.

[0009] The photoelectric sensor module includes multiple photoelectric sensors arranged longitudinally according to the Cun, Guan, and Chi positions, and is used to detect multimodal physiological parameters such as human pulse rate, blood pressure, blood oxygen, and electrocardiogram, and is connected to the first processing unit signal;

[0010] The first processing unit is used to process the signal from the photoelectric sensor module and the signal from the second processing unit, and communicate with the second wireless communication unit through the first wireless communication unit and connect with the second processing unit signal;

[0011] The second processing unit is used to process the signal sent by the first processing unit, control the display module and the voice interaction module, communicate with the first wireless communication unit through the second wireless communication unit and connect the signal of the first processing unit; communicate with the third wireless communication unit through the second wireless communication unit and connect the signal of the third processing unit; and connect with the wireless network signal through the second wireless communication unit to access the cloud platform;

[0012] The host module can be equipped with multiple sensor modules.

[0013] The voice interaction module can realize human-computer dialogue with artificial intelligence big data models;

[0014] The cloud platform includes a server, an artificial intelligence big data model, and a traditional Chinese medicine standard pulse chart database.

[0015] As a preferred solution, the second processing unit includes a data extraction module, a purification module, and a statistics module.

[0016] As a preferred solution, the artificial intelligence big data model uses a feature comparison method to compare with the data in the traditional Chinese medicine standard pulse chart database, and returns the comparison results and treatment recommendations.

[0017] As a preferred solution, the TCM standard pulse chart database data includes 28 types of pulse chart data.

[0018] As a preferred solution, the artificial intelligence big data model is an artificial intelligence big data model of traditional Chinese medicine in the industry big model.

[0019] As a preferred solution, the sensor module can detect the pulse of the left and right hands.

[0020] As a preferred solution, the working time of the sensor module is greater than 1 minute.

[0021] As a preferred solution, the display module adopts touch control.

[0022] As a preferred solution, the working method of the medical artificial intelligence pulse diagnosis system based on photoplethysmography is

[0023] The method comprises the following steps:

[0024] In the first step, the sensor module monitors multimodal physiological parameters such as pulse rate, blood pressure, blood oxygen, and electrocardiogram;

[0025] In the second step, the first processing unit transmits the data collected by the photoelectric sensor module to the second wireless communication unit via the first wireless communication unit;

[0026] In a third step, the second wireless communication unit transmits the signal to the second wireless communication unit;

[0027] Step 4: The second wireless communication unit receives the signal and transmits it to the second processing unit;

[0028] In the fifth step, the second processing unit performs data processing through the set extraction module, purification module, and statistical module;

[0029] Step 6: The second processing unit transmits the processed data to the cloud platform via the second wireless communication unit;

[0030] In the seventh step, the AI ​​model deployed on the cloud platform compares the received data with the data in the TCM standard pulse chart database;

[0031] Step 8: If the comparison is unsuccessful, the cloud platform transmits the incorrect monitoring result back to the second wireless communication unit;

[0032] Step 9: The second wireless communication unit transmits the data to the second processing unit;

[0033] Step 10: The second processing unit processes the signal and transmits it to the second wireless communication unit;

[0034] In step 11, the second wireless communication unit transmits the data to the first wireless communication unit;

[0035] In step 12, the first wireless communication unit transmits the information to the first processing unit, and the sensor module re-monitors.

[0036] Step 13: If the comparison is successful, the second wireless communication unit receives the monitoring correct signal;

[0037] Step 14: The second wireless communication unit transmits the data to the second processing unit;

[0038] Step 15: The second processing unit processes the signal;

[0039] Step 16: The second processing unit sends a control instruction to the display module;

[0040] Step 17: The display module displays the monitoring results, pulse waveform, blood pressure, blood oxygen and other physiological parameters;

[0041] Step 18: The second processing unit sends a control instruction to the voice interaction module;

[0042] In the 19th step, the voice interaction module broadcasts the monitoring results and treatment recommendations;

[0043] In step 20, the second processing unit sends a control instruction to the third wireless communication unit;

[0044] In step 21, the third wireless communication unit transmits the instruction to the third processing unit;

[0045] Step 22: The third processing unit issues a print instruction;

[0046] Step 23: The printing module prints the monitoring results and treatment recommendations.

[0047] As a preferred solution, in step 17, the pulse wave waveform is displayed as a Fourier fitting curve.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. The present invention uses a photoelectric sensor based on the photoelectric volumetric pulse wave medical artificial intelligence pulse diagnosis system technology, which is more precise, has more parameters, and a more accurate fitting curve, providing doctors with a guarantee of pulse diagnosis accuracy.

[0050] 2. The present invention provides an artificial intelligence big data model for comparing standard pulse diagrams based on photoplethysmography medical artificial intelligence pulse diagnosis system, which avoids the long-standing drawbacks of subjective judgment by doctors and is more objective and accurate.

[0051] 3. The medical artificial intelligence pulse diagnosis system based on photoplethysmography of the present invention can provide treatment suggestions through artificial intelligence big data models, reduce the workload of doctors, and provide patients with more accurate treatment plans.

[0052] 4. The present invention's medical artificial intelligence pulse diagnosis system based on photoplethysmography changes the previously invisible process of traditional Chinese medicine pulse diagnosis into a digital, visual, and intelligent one, transforming traditional manual monitoring to digital intelligent monitoring, and from single parameter to multi-parameter comprehensive monitoring, achieving a leapfrog technological upgrade, which is of great help to both doctors and patients.

[0053] 5. The medical artificial intelligence pulse diagnosis system based on photoplethysmography of the present invention is highly practical, with automated and intelligent operation, which can greatly reduce the workload of doctors in outpatient clinics and clinics, improve diagnosis and treatment efficiency, and increase patient satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The following is a brief description of the drawings used in this application. Obviously, the following drawings are only used to explain the concept of the present invention and are not intended to limit the present invention.

[0055] Figure 1 It is a schematic diagram of the module structure of an embodiment of the medical artificial intelligence pulse diagnosis system based on photoplethysmography of the present invention.

[0056] Figure 2 It is a schematic diagram of the sensor module structure of an embodiment of the medical artificial intelligence pulse diagnosis system based on photoplethysmography of the present invention.

[0057] Figure 3 It is a schematic diagram of the host module structure of an embodiment of the medical artificial intelligence pulse diagnosis system based on photoplethysmography of the present invention.

[0058] Figure 4 It is a schematic diagram of the printing module structure of an embodiment of the medical artificial intelligence pulse diagnosis system based on photoplethysmography of the present invention.

[0059] Figure 5 It is a flow chart of the working method of an embodiment of the medical artificial intelligence pulse diagnosis system based on photoplethysmography of the present invention. DETAILED DESCRIPTION

[0060] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary schemes listed for the purpose of illustrating the present invention and are intended to be used for explaining the present invention and are not to be construed as limiting or restricting the present invention.

[0061] Figure 1This is a module structure diagram of a medical artificial intelligence pulse diagnosis system based on photoplethysmography provided by an embodiment of the present invention. As shown in the figure,

[0062] It includes a sensor module 1, a host module 2, a printing module 3, and a cloud platform 4.

[0063] The sensor module 1 is used to monitor the patient's pulse rate, blood pressure, blood oxygen and other physiological parameters.

[0064] The host module 2 processes the data transmitted by the sensor module 1 and then transmits it to the cloud platform 4. At the same time, it displays physiological values ​​such as pulse waveform, blood pressure, and blood oxygen through the display module 23. After receiving the results from the cloud platform 4, it controls the printing module 3 to print out the monitoring results and output voice information through the voice interaction module.

[0065] The printing module 3 prints out the diagnosis and treatment results.

[0066] Cloud Platform 4 includes servers, AI big data models, and a TCM standard pulse chart database, which analyzes and compares pulse charts and transmits results and treatment recommendations. The TCM standard pulse chart database uses standard pulse charts and digitizes them to form a database, providing the basic conditions for AI big data model comparison.

[0067] With the continuous development and evolution of AI big data model technology, it has now been applied in the field of medical-assisted diagnosis. The number of AI big models in China is substantial and continues to grow. According to data released by the Cyberspace Administration of China, 346 AI big models have successfully passed registration, 101 of which are in the medical field. There are also AI big data models dedicated to intelligent diagnosis in Traditional Chinese Medicine (TCM). For example, Qihuang Wendao (Qihuang Wendao) boasts a dataset of over 10 million ancient TCM texts and medical records, enabling intelligent prescription pairing. According to feedback from pilot hospitals, its prescriptions have a consistency rate exceeding 90% with those of chief physicians. Furthermore, Digital Qihuang 2.0, with its advanced multimodal capabilities and extensive knowledge base, represents a new breakthrough in intelligent diagnosis and treatment. It achieves significant improvements in data scale and model capabilities. Digital Qihuang 2.0 includes a large-scale model with 32 billion parameters, covering two key modules: Traditional Chinese Medicine (TCM) and Western Medicine (Wendao), with over 200,000 and 100,000 instruction data, respectively. The model covers more than 80,000 traditional Chinese medicine prescriptions, more than 40,000 traditional Chinese medicine ingredients, more than 9,000 kinds of traditional Chinese medicine, more than 2,000 traditional Chinese medicine syndromes, and more than 1,000 ancient books; more than 18,000 targets, more than 2,000 diseases, 2.4 million compounds (of which about 410,000 are natural products), and more than 2 million documents. Based on the cross-modal fusion of images, text, voice, and biosignals, Digital Qihuang 2.0 achieves accurate understanding and reasoning of complex medical scenarios. For example, in traditional Chinese medicine diagnosis and treatment, the model can integrate the patient's tongue image, pulse diagnosis data, and voice description of symptoms to generate accurate syndrome diagnosis and personalized prescription recommendations, significantly improving clinical diagnosis and treatment efficiency. At the same time, Digital Qihuang 2.0 has added innovative expert system modules. These modules are built on a multi-domain deep knowledge base and achieve efficient and accurate professional question-and-answer and problem-solving capabilities. Through the voice interaction module 24 set in the host 2, both doctors and patients can ask and answer questions with the artificial intelligence big data model, greatly facilitating diagnosis and treatment work. The use of such traditional Chinese medicine artificial intelligence big data models meets the requirements of the present invention.

[0068] Figure 2 This is a schematic diagram of the structure of the sensor module of the medical artificial intelligence pulse diagnosis system based on photoplethysmography provided by an embodiment of the present invention. As shown in the figure, the sensor module 1 includes a sensor front cover 11, a sensor back cover 12, a sensor base 13, a photoelectric sensor module 14, a first processing unit 15, a first wireless communication unit 16, a first battery module 17, a light-transmitting sheet 18, and a touchpad 19. The photoelectric sensor module 14, the first processing unit 15, the first wireless communication unit 16, the first battery module 17, the light-transmitting sheet 18, and the touchpad 19 can all be detachably arranged in the accommodating cavity formed by the sensor front cover 11 and the sensor back cover 12.

[0069] Photoelectric sensor 13 uses a photoplethysmography (PPG) sensor, a non-invasive physiological signal detection technology based on optical detection of blood flow changes. Its signal consists of a periodic pulse wave (AC component) and background tissue information (DC component), from which a variety of health-related physiological indicators can be extracted. It is widely used to monitor vital signs such as heart rate, blood oxygen saturation (SpO2), respiratory rate, and blood pressure, and plays a particularly important role in the fields of smart wearable devices and mobile health (mHealth). The core components of a PPG sensor are a light source and a photodetector. The light source is an LED, with wavelengths selectable from red, green, near-infrared, or full-spectrum white light. The photodetector is an avalanche photodiode (APD), a special photodiode with internal gain generated by applying a reverse voltage. It converts optical signals into electrical signals and features high gain, low noise, and high sensitivity. Compared to ordinary photodiodes, it has a higher signal-to-noise ratio (SNR), faster response, low dark current, and high sensitivity. The wavelength response range is typically between 200 and 1150 nm. Depending on the specific location of use, the PPG sensor can be reflective. The working principle of reflective PPG is: the light source illuminates the skin tissue, and the photodetector receives the reflected light from the tissue surface. The blood flow is analyzed by analyzing the changes in the reflected light. It is often used for monitoring positions such as the wrist and forearm.

[0070] For example, the GH3300 multi-channel PPG health monitoring chip has monitoring functions such as heart rate (HR), heart rate variability (HRV) and blood oxygen saturation (SpO2), and has the characteristics of ultra-low power consumption and ultra-high precision. Parameters: It has 4 independent PPG channels and supports synchronous sampling to reduce power consumption; it supports 4 differential PD or 8 single-ended PD multiplexing PPG channels, and 4 high-precision 24-bit ADC; ultra-low dark current noise of <14pArms; 4 LED drivers, supporting up to 16 LEDs, with a peak current of 200mA per driver; a maximum system SNR of 110dB ensures high-precision PPG measurement; supports up to 128 samples per sampling cycle, with an ambient light rejection ratio of 80dB; 1 DC or AC mode ECG channel, supporting single-lead ECG acquisition; DC mode equivalent input noise: 1.67μVrms (1x gain); AC mode equivalent input noise: 1.25μVrms (20x gain); input impedance of 10GΩ, DC input range of ±1300mV, supporting dry electrode applications, automatic LED dimming and automatic gain control; supports SPI and IIC communication; package adopts WLCSP, 49 pins, 0.35mm pitch, 2.645mm*2.645mm*0.5mm; large-capacity FIFO, area 1:16k Byte (3.2k sampling points), area 2: 80kByte (16k sampling points).

[0071] The multiple photoelectric sensors in the photoelectric sensor module 14 are arranged longitudinally at the corresponding Cun, Guan, and Chi points. Their centerlines are offset 16-20 mm from the center of the arc formed by the sensor front cover 11 and sensor rear cover 12 to align with the radial artery at the wrist. When monitoring the left hand, the sensor front cover 11 faces the patient; when monitoring the right hand, the sensor rear cover 12 faces the patient. This accommodates the different eccentricities of the radial arteries in the left and right hands.

[0072] Figure 3 2 is a schematic diagram of the structure of a host module of a medical artificial intelligence pulse diagnosis system based on photoplethysmography provided by an embodiment of the present invention. As shown in the figure, the host module includes a body 21, a bottom cover 22, a display module 23, a voice interaction module 24, a second processing unit 25, a second wireless communication unit 26, a power switch 27, a power button 28, and a decorative panel 29. The voice interaction module 24, the second processing unit 25, the second wireless communication unit 26, and the power switch 27 are all detachably disposed in the accommodating cavity formed by the body and the bottom cover.

[0073] Display module 23 includes a lens 231, a touchscreen LCD 232, a driver board 233, and a display back cover 234. Both the touchscreen LCD 232 and the driver board 233 are removably mounted within the cavity formed by the lens 231 and the display back cover 234. The touchscreen LCD 232 used in display module 23 is an 8-inch TFT LCD with a resolution of 1200 x 1920 pixels, a contrast ratio of 1000:1, a module brightness of 330 Cd / m², and a full viewing angle, making it easy for doctors to operate.

[0074] The second wireless communication unit 26 adopts a Bluetooth wireless gateway, which includes a central processing unit, a Bluetooth function module, a network communication module, and a power management module. For example, the Bluetooth gateway model M1000 can achieve long-distance transmission: stable data transmission can be achieved within a range of 300 meters, and the indoor environment can penetrate two walls with a coverage radius of up to 40 meters, which facilitates the movement of the sensor module 1 according to the use environment, including separation from the host 2 upstairs and downstairs, and between the reception desk and the doctor's office, providing great convenience; one-to-many data collection: supports two-way communication and supports up to 40 devices to access simultaneously, which makes it possible for one host 2 to carry several or even dozens of sensor modules 1, creating technical conditions for equipping clinics and examination rooms with comprehensive and complete pulse diagnosis systems. It can realize a one-master and multiple-split mode with one host at the reception desk and one sensor module 1 in each doctor's office, which will greatly reduce the configuration cost of clinics and medical institutions and enhance the application and promotion of the present invention; and it has edge computing capabilities, which facilitates the artificial intelligence big data processing set by the cloud platform 4 to send data packets, reduce the pressure on cloud computing, and achieve energy saving and consumption reduction through efficient data processing and management models.

[0075] The monitoring data processing includes an extraction module, a purification module, and a statistical module, which respectively extract feature data of the pulse and other parameter data obtained by the photoelectric sensor module 14, and perform screening and purification, eliminate some unqualified data, and then perform statistical normalization on them, optimize computing efficiency, enhance its interpretability, and provide favorable conditions for displaying waveforms and transmitting them to the artificial intelligence big data model analysis on the cloud platform 4.

[0076] The voice interaction module 24 provides voice interaction function between doctors, patients and artificial intelligence big data models. Doctors and patients can transmit information such as symptoms, tongue diagnosis, facial diagnosis, physical diagnosis, etc. to the artificial intelligence big data model. The artificial intelligence big data model analyzes and provides diagnostic references and treatment suggestions, which can greatly facilitate the doctor's diagnosis and treatment work, reduce their work intensity and pressure, and provide better treatment services for patients.

[0077] Figure 43 is a schematic diagram of the structure of a printing module of a medical artificial intelligence pulse diagnosis system based on photoplethysmography provided by an embodiment of the present invention. As shown in the figure, the printing module includes a printer upper cover 31, a printer lower cover 32, a third processing unit 33, a third wireless communication unit 34, a printing paper module 35, a print head module 36, and a second battery module 37. The third processing unit 33, the third wireless communication unit 34, the printing paper module 35, the print head module 36, and the second battery module 37 are all detachably disposed in the accommodating cavity formed by the printer upper cover and the printer lower cover.

[0078] The printing module adopts the widely used wireless Bluetooth printing technology, which is a common technology and will not be described in detail here.

[0079] Figure 5 This is a flow chart of the working method of an embodiment of the medical artificial intelligence pulse diagnosis system based on photoplethysmography of the present invention. When the system starts working:

[0080] First, execute step S11 to turn on the touch switch of the sensor module and place the photoelectric sensor module in contact with the Cun, Guan, and Chi positions of the radial artery on the patient's wrist to monitor multimodal physiological parameters such as pulse rate, blood pressure, blood oxygen, and electrocardiogram;

[0081] Executing step S12, the first processing unit transmits the data collected by the photoelectric sensor module to the second wireless communication unit via the first wireless communication unit;

[0082] Executing step S13, the second wireless communication unit transmits the signal to the second wireless communication unit;

[0083] Executing step S14, the second wireless communication unit receives the signal and transmits it to the second processing unit;

[0084] Executing step S15, the second processing unit performs data processing through the configured extraction module, purification module, and statistical module;

[0085] Executing step S16, the second processing unit transmits the processed data to the cloud platform via the second wireless communication unit;

[0086] Executing step S17, the artificial intelligence model deployed on the cloud platform compares the received data with the data in the TCM standard pulse chart database;

[0087] Execute step S18, if the comparison is unsuccessful, the cloud platform returns the incorrect monitoring result to the second wireless communication unit;

[0088] Executing step S19, the second wireless communication unit transmits the data to the second processing unit;

[0089] Executing step S20, the second processing unit processes the signal and transmits it to the second wireless communication unit;

[0090] Executing step S21, the second wireless communication unit transmits to the first wireless communication unit;

[0091] Executing step S22, the first wireless communication unit transmits the information to the first processing unit, and the sensor module re-monitors;

[0092] Executing step S23, if the comparison is successful, the second wireless communication unit receives the monitoring correct signal;

[0093] Executing step S24, the second wireless communication unit transmits the data to the second processing unit;

[0094] Executing step S25, the second processing unit processes the signal;

[0095] Executing step S26, the second processing unit sends a control instruction to the display module;

[0096] Executing step S27, the display module displays the monitoring results, pulse waveform, blood pressure, blood oxygen and other physiological parameters;

[0097] Executing step S28, the second processing unit sends a control instruction to the voice interaction module;

[0098] Execute step S29, the voice interaction module broadcasts the monitoring results and treatment recommendations;

[0099] Executing step S30, the second processing unit sends a control instruction to the third wireless communication unit;

[0100] Executing step S31, the third wireless communication unit transmits the instruction to the third processing unit;

[0101] Executing step S32, the third processing unit issues a print instruction;

[0102] Execute step S33, the printing module prints the monitoring results and treatment recommendations.

[0103] The above describes the implementation of the present invention's artificial intelligence pulse diagnosis system based on photoplethysmography. The specific features of the present invention's artificial intelligence pulse diagnosis system based on photoplethysmography can be specifically designed based on the functions of the features disclosed above, and these designs are all within the capabilities of those skilled in the art. Furthermore, the various technical features disclosed above are not limited to combinations with other features disclosed above. Those skilled in the art may also combine the various technical features in other ways, based on the objectives of the present invention, to achieve the objectives of the present invention.

Claims

1. A medical artificial intelligence pulse diagnosis system based on photoplethysmography and its working method, characterized by: The present invention comprises a sensor module, a host module, a printing module and a cloud platform. The sensor module comprises a sensor front cover, a sensor back cover, a sensor base, a photoelectric sensor module, a first processing unit, a first wireless communication unit, a first battery module, a lens and a touchpad. The sensor module, the first processing unit, the first wireless communication unit, the first battery module, the lens and the touchpad are all detachably arranged in the accommodating cavity formed by the sensor front cover and the sensor back cover. The host module comprises a body, a bottom cover, a display module, a voice interaction module, a second processing unit, a second wireless communication unit, a power switch, a power button and a decorative panel. The voice interaction module, the second processing unit, the second wireless communication unit and the power switch are all detachably arranged in the accommodating cavity formed by the body and the bottom cover. The printing module comprises a printer upper cover, a printer lower cover, a third processing unit, a third wireless communication unit, a printing paper module, a print head module and a second battery module. The third processing unit, the third wireless communication unit, the printing paper module, the print head module and the second battery module are all detachably arranged in the accommodating cavity formed by the printer upper cover and the printer lower cover. The photoelectric sensor module includes multiple photoelectric sensors arranged longitudinally according to the Cun, Guan, and Chi positions, and is used to detect multimodal physiological parameters such as human pulse rate, blood pressure, blood oxygen, and electrocardiogram, and is connected to the first processing unit signal; The first processing unit is used to process the signal from the photoelectric sensor module and the signal from the second processing unit, and communicate with the second wireless communication unit through the first wireless communication unit and connect with the second processing unit signal; The second processing unit is used to process the signal sent by the first processing unit, control the display module and the voice interaction module, communicate with the first wireless communication unit through the second wireless communication unit and connect the signal of the first processing unit; communicate with the third wireless communication unit through the second wireless communication unit and connect the signal of the third processing unit; and connect with the wireless network signal through the second wireless communication unit to access the cloud platform; The host module can be equipped with multiple sensor modules. The voice interaction module can realize human-computer dialogue with artificial intelligence big data models; The cloud platform includes a server, an artificial intelligence big data model, and a traditional Chinese medicine standard pulse chart database.

2. The medical artificial intelligence pulse diagnosis system based on photoplethysmography according to claim 1, characterized in that: The second processing unit includes a data extraction module, a purification module, and a statistics module.

3. The medical artificial intelligence pulse diagnosis system based on photoplethysmography according to claim 1, characterized in that: The artificial intelligence big data model uses a feature comparison method to compare with the data in the traditional Chinese medicine standard pulse chart database, and returns the comparison results and treatment recommendations.

4. The medical artificial intelligence pulse diagnosis system based on photoplethysmography according to claim 1, characterized in that: The TCM standard pulse chart database data includes 28 types of pulse chart data.

5. The medical artificial intelligence pulse diagnosis system based on photoplethysmography according to claim 1 is characterized by: The artificial intelligence big data model is an artificial intelligence big data model of traditional Chinese medicine in the industry big model.

6. The medical artificial intelligence pulse diagnosis system based on photoplethysmography according to claim 1, characterized in that: The sensor module can detect the pulse of the left and right hands.

7. The medical artificial intelligence pulse diagnosis system based on photoplethysmography according to claim 1, characterized in that: The sensing module operates for more than 1 minute.

8. The medical artificial intelligence pulse diagnosis system based on photoplethysmography according to claim 1, characterized in that: The artificial intelligence big data model can also be deployed locally.

9. A method for operating a medical artificial intelligence pulse diagnosis system based on photoplethysmography, characterized by: The method comprises the following steps: In the first step, the sensor module monitors multimodal physiological parameters such as pulse rate, blood pressure, blood oxygen, and electrocardiogram; In the second step, the first processing unit transmits the data collected by the photoelectric sensor module to the second wireless communication unit via the first wireless communication unit; In a third step, the second wireless communication unit transmits the signal to the second wireless communication unit; In the fourth step, the second wireless communication unit receives the signal and transmits it to the second processing unit; the signal is processed by the extraction module, purification module, and statistical module; In the fifth step, the second processing unit performs data processing through the set extraction module, purification module, and statistical module; Step 6: The second processing unit transmits the processed data to the cloud platform via the second wireless communication unit; In the seventh step, the AI ​​model deployed on the cloud platform compares the received data with the data in the TCM standard pulse chart database; Step 8: If the comparison is unsuccessful, the cloud platform transmits the incorrect monitoring result back to the second wireless communication unit; Step 9: The second wireless communication unit transmits the data to the second processing unit; Step 10: The second processing unit processes the signal and transmits it to the second wireless communication unit; In step 11, the second wireless communication unit transmits the data to the first wireless communication unit; In step 12, the first wireless communication unit transmits the information to the first processing unit, and the sensor module re-monitors. Step 13: If the comparison is successful, the second wireless communication unit receives the monitoring correct signal; Step 14: The second wireless communication unit transmits the data to the second processing unit; Step 15: The second processing unit processes the signal; Step 16: The second processing unit sends a control instruction to the display module; Step 17: The display module displays the monitoring results, pulse waveform, blood pressure, blood oxygen and other physiological parameters; Step 18: The second processing unit sends a control instruction to the voice interaction module; In the 19th step, the voice interaction module broadcasts the monitoring results and treatment recommendations; In step 20, the second processing unit sends a control instruction to the third wireless communication unit; In step 21, the third wireless communication unit transmits the instruction to the third processing unit; Step 22: The third processing unit issues a print instruction; Step 23: The printing module prints the monitoring results and treatment recommendations.

10. The working method of the medical artificial intelligence pulse diagnosis system based on photoplethysmography according to claim 9, characterized in that: In step 17, the pulse wave waveform is displayed as a Fourier fitting curve.

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