Pulse condition dialectical method and system, storage medium and electronic equipment

Abstract

The invention provides a pulse condition dialectical method and system, a storage medium and electronic equipment, and the method comprises the following steps: collecting pulse condition data, and obtaining characteristic parameters of the pulse condition data; fitting the pulse condition data and establishing a pulse condition characteristic value set database based on a pulse condition quantification standard; constructing and training a pulse condition quantification mathematical model based on the pulse condition characteristic value set database; a pulse condition oscillogram of the dialectical person is obtained; and inputting the pulse condition oscillogram of the dialectical person into the pulse condition quantitative mathematical model to obtain a pulse condition dialectical result. According to the pulse condition dialectical method and system, the storage medium and the electronic equipment, the modern sensing technology, the signal processing algorithm and the traditional Chinese medicine pulse diagnosis theory are combined, precision, digitization and intelligentization of pulse diagnosis are achieved, the diagnosis accuracy and efficiency are remarkably improved, and a foundation is laid for modernization and internationalization development of traditional Chinese medicine pulse diagnosis.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and particularly relates to a pulse condition dialectical method, system, storage medium and electronic device. Background Art

[0002] In recent years, with the progress of technology, the research on using pressure sensors to simulate traditional Chinese medicine pulse diagnosis has increased. These studies analyze the relationship between pulse waves and applied pressure by obtaining pressure pulse diagrams, and draw curves to determine the most appropriate pressure for taking the pulse. This method can analyze pulse diagrams at different pressures, thereby revealing some properties of pulse conditions, making pressure sensors an important tool for the objectification of traditional Chinese medicine pulse diagnosis.

[0003] However, due to the complexity of pulse conditions and the differences in pulse diagnosis methods and cognitions, it is difficult to form a unified and objective analysis standard for traditional Chinese medicine pulse conditions. Fortunately, through the summary of the clinical experience of countless traditional Chinese medicine experts, we have the basic guidelines for pulse conditions: floating and sinking; slow and rapid; deficient and excessive; slippery and astringent. These guidelines concisely summarize the complicated pulse conditions and provide a general standard for pulse condition analysis.

[0004] The core of traditional Chinese medicine pulse diagnosis lies in detecting pulse fluctuation information at the "Cunkou" artery at different pressures. Pulse diagnosis information is actually a collection of multi-dimensional signals such as pressure signal for taking the pulse and the movement signal of the radial artery itself. The development of modern sensor technology makes it possible to collect and analyze personal pulse information using sensors. Through the precise dialectics of pressure sensors, we can quickly extract the pulse information of the person to be dialectically analyzed, and conduct in-depth analysis through pulse movement information acquisition and analysis software to provide reference for clinical practice.

[0005] Currently, based on the data collected by three pressure sensors at Cun, Guan, and Chi, through the analysis and induction of the data, we can more accurately understand personal pulse condition information. At the same time, it is possible to dialectically analyze the pulse units at the Cun, Guan, and Chi positions respectively at the same moment, compare the differences in pulse units between the three positions, making it possible to analyze pulse condition data, greatly improving the accuracy of pulse diagnosis and reducing the time and error of manual dialectics.

[0006] Although existing pulse sensors can collect pulse information under different conditions at the radial artery through the arrangement and combination of three pressure sensors, due to the lack of an objective, effective, and up-to-date traditional Chinese medicine theory for analysis and guidance, a large amount of the collected information fails to be scientifically summarized, resulting in deviations in pulse condition feedback. Therefore, in recent years, various pulse diagnosis instruments with different performances have been developed in our country. These pulse diagnosis instruments detect the analog signals of pulse waves through sensors, including various types of sensors. In the domestic market, traditional Chinese medicine pulse condition instruments mainly use pressure sensing technology as the core, and draw a curve graph by collecting the pressure changes during the pulse activity of the radial artery to obtain the pulse condition. However, this method of obtaining the pulse condition is relatively single and has certain errors. In addition, there is no unified standard for the analysis of the measurement data of the pulse condition instrument, resulting in difficulty in meeting the requirements of standardization and structuring of the test data, bringing many difficulties to the processing of pulse condition information.

[0007] Therefore, how to provide a method for pulse condition dialectics to solve the above problems has become an urgent technical problem for those skilled in the art. Summary of the Invention

[0008] In view of the above-mentioned disadvantages of the prior art, the purpose of the present invention is to provide a method, system, storage medium, and electronic device for pulse condition dialectics, which improve the accuracy and objectivity of pulse diagnosis, realize the quantification and intelligence of pulse diagnosis data, promote the integration of traditional Chinese medicine theory and modern technology, improve the diagnosis efficiency and patient experience, and further promote the internationalization and standardization of traditional Chinese medicine pulse diagnosis.

[0009] In the first aspect, the present invention provides a method for pulse condition dialectics, and the method includes the following steps: collecting pulse condition data and obtaining characteristic parameters of the pulse condition data;

[0010] Based on the pulse condition quantification standard, fitting the pulse condition data and establishing a pulse condition eigenvalue set database;

[0011] Based on the pulse condition eigenvalue set database, constructing and training a pulse condition quantification mathematical model;

[0012] Obtaining the pulse waveform diagram of the person to be dialectized;

[0013] Inputting the pulse waveform diagram of the person to be dialectized into the pulse condition quantification mathematical model to obtain the pulse condition dialectics result.

[0014] In one implementation manner of the first aspect, dynamic pressure application is implemented through a hybrid pressure sensor array to collect pulse condition data.

[0015] In one implementation manner of the first aspect, based on the pulse condition quantification standard, the pulse condition data is divided into 27 pulse condition types, and each pulse condition type is analyzed and the corresponding eigenvalue set is obtained to establish a pulse condition eigenvalue set database.

[0016] In an implementation of the first aspect, inputting the pulse waveform diagram of the person to be dialectically diagnosed into the pulse quantification mathematical model to obtain the pulse dialectical result includes the following steps:

[0017] Analyze the pulse waveform diagram of the person to be dialectically diagnosed by wavelet analysis method to obtain the characteristic parameters of the pulse data of the person to be dialectically diagnosed;

[0018] Input the characteristic parameters of the pulse data of the person to be dialectically diagnosed into the pulse quantification mathematical model to obtain the corresponding pulse type;

[0019] Combine clinical information to analyze and judge the pulse type to generate a comprehensive decision of pulse and syndrome.

[0020] In an implementation of the first aspect, the characteristic parameters include: main wave height, tidal wave height, nadir height, dicrotic wave height, maximum wave peak value in the first low-pressure area, maximum wave peak value in the second low-pressure area, time value from the starting point of the pulse wave diagram to the main wave peak point, time value from the starting point of the pulse wave diagram to the nadir, time value from the nadir to the end point of the pulse wave diagram, time value from the starting point to the end point of the pulse wave diagram, first main wave width, second main wave width, mean pulse rate, pulse rate stability, pulse rate variability, mean of mean pulse cycle differences, stability coefficient of mean pulse cycle differences, initial pressure value of the pressure sensor probe, maximum downward pressure value of the pressure sensor probe, main wave peak curvature.

[0021] In an implementation of the first aspect, the pulse quantification mathematical model adopts a pulse judgment matrix algorithm.

[0022] In an implementation of the first aspect, after inputting the pulse waveform diagram of the person to be dialectically diagnosed into the pulse quantification mathematical model to obtain the pulse dialectical result, it further includes generating a treatment plan based on the pulse dialectical result.

[0023] In the second aspect, the present invention provides a pulse dialectical system, which includes an acquisition module, a fitting module, a quantification module, an acquisition module, and a dialectical module;

[0024] The acquisition module is used to acquire pulse data and obtain the characteristic parameters of the pulse data;

[0025] The fitting module is used to fit the pulse data based on the pulse quantification standard and establish a pulse characteristic value set database;

[0026] The quantification module is used to construct and train a pulse quantification mathematical model based on the pulse characteristic value set database;

[0027] The acquisition module is used to acquire the pulse waveform diagram of the person to be dialectically diagnosed;

[0028] The dialectical module is used to input the pulse waveform diagram of the person to be dialectically diagnosed into the pulse quantification mathematical model to obtain the pulse dialectical result.

[0029] In a third aspect, the present invention provides an electronic device, which includes: a processor and a memory;

[0030] The memory is used to store a computer program;

[0031] The processor is used to execute the computer program stored in the memory, so that the electronic device executes the above-mentioned pulse dialectical method.

[0032] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by an electronic device, the above-mentioned pulse dialectical method is realized.

[0033] As described above, the pulse dialectical method, system, storage medium and electronic device of the present invention have the following beneficial effects:

[0034] 1. Improved accuracy: Through modern sensor technology and signal processing algorithms, the present invention can accurately collect and analyze pulse waveforms, reduce the subjective errors that may occur in traditional manual pulse diagnosis, and improve the accuracy of pulse diagnosis.

[0035] 2. Enhanced objectivity: Using algorithms such as deep learning to quantitatively analyze pulse data makes the pulse diagnosis results more objective and reproducible, which helps to standardize traditional Chinese medicine pulse diagnosis.

[0036] 3. Data digitization: The present invention converts complex pulse information into digital signals, which is convenient for storage, transmission and analysis, and provides a basis for further research and application of pulse diagnosis data.

[0037] 4. Intelligent analysis: Combining machine learning and artificial intelligence technologies, the present invention can automatically identify and predict pulse patterns, provide decision-making support for doctors, and realize the intelligence of pulse diagnosis.

[0038] 5. Theoretical integration: On the basis of respecting and inheriting traditional Chinese medicine theory, the present invention innovatively applies modern technology to the field of pulse diagnosis, promoting the deep integration of traditional Chinese medicine theory and modern technology.

[0039] 6. Improved efficiency: The automated pulse diagnosis process reduces the workload of doctors, improves the diagnosis efficiency, and enables doctors to focus more on disease analysis and treatment decision-making.

[0040] 7. Improved patient experience: The non-invasive pulse diagnosis device reduces patient discomfort, and the friendly user interface and fast diagnosis process enhance the patient's medical experience.

[0041] 8. International Promotion: The technical solution of the present invention helps to promote traditional Chinese medicine pulse diagnosis to the international market, enabling more people to understand and accept the unique diagnosis and treatment methods of traditional Chinese medicine.

[0042] 9. Standardized Development: By establishing unified pulse diagnosis data standards and analysis processes, the present invention promotes the development of traditional Chinese medicine pulse diagnosis towards standardization and regularization.

[0043] In summary, by combining modern sensing technology, signal processing algorithms with traditional Chinese medicine pulse diagnosis theory, the present invention realizes the precision, digitization and intelligence of pulse diagnosis, not only improving the accuracy and efficiency of diagnosis, but also laying a foundation for the modernization and internationalization of traditional Chinese medicine pulse diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It shows a flowchart of the pulse syndrome differentiation method of the present invention in an embodiment;

[0045] Figure 2 It shows a core flowchart of the pulse quantification research and judgment algorithm of the present invention;

[0046] Figure 3 It shows a digital device diagram of the pulse syndrome differentiation method of the present invention in an embodiment;

[0047] Figure 4 It shows a structural schematic diagram of the pulse syndrome differentiation system of the present invention in an embodiment;

[0048] Figure 5 It shows a structural schematic diagram of the electronic device of the present invention in an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0049] The following specifically illustrates the implementation manners of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0050] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Therefore, only the components related to the present invention are shown in the diagrams, rather than being drawn according to the number, shape and size of the components in actual implementation. The types, quantities and proportions of the components in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0051] With the development of digital and intelligent technologies, traditional Chinese medicine pulse diagnosis has been continuously integrated with sensing technologies and modern data platforms, injecting new vitality into the development of traditional Chinese medicine modernization. The digital and intelligent pulse diagnosis process is a typical artificial intelligence processing process, which includes the entire thinking process of information acquisition - analysis - quantitative processing - feedback - evaluation - synthesis. Therefore, applying artificial intelligence technology to traditional Chinese medicine diagnosis and treatment can promote the leapfrog development of traditional Chinese medicine diagnosis and treatment technologies and solve the main problems in the modern development of traditional Chinese medicine diagnosis and treatment.

[0052] Traditional Chinese medicine syndrome differentiation is one of the basic principles for traditional Chinese medicine to understand and treat diseases, which mainly includes the process of syndrome differentiation and treatment. Syndrome differentiation means that by comprehensively analyzing information such as the patient's clinical symptoms, signs, and pulse conditions, to identify the nature, location, cause, and pathological changes of the disease, so as to obtain the current disease location and nature of the disease. The process of syndrome differentiation includes two key steps: syndrome identification and treatment. Syndrome identification is to identify the syndromes of the patient, that is, to collect the patient's information through methods such as observation, auscultation and olfaction, interrogation, and palpation, and then analyze this information according to traditional Chinese medicine theory to determine the syndromes of the patient. Syndrome is a unique concept in traditional Chinese medicine, which includes a general description of the pathological changes at a certain stage in the occurrence and development of the disease. Treatment is to formulate a treatment plan based on the identified syndromes according to the treatment principles and methods of traditional Chinese medicine. Traditional Chinese medicine treats diseases not only focusing on local lesions, but emphasizes the overall concept and the balance of yin and yang. Therefore, the treatment plan usually includes various treatment means, such as traditional Chinese medicine, acupuncture, massage, etc.

[0053] The present invention uses modern scientific and technological means to verify and quantify the syndrome differentiation process of traditional Chinese medicine, in order to improve the accuracy and scientific nature of traditional Chinese medicine treatment.

[0054] Next, the technical solutions in the embodiments of the present invention will be described in detail with reference to the accompanying drawings in the embodiments of the present invention.

[0055] As Figure 1 shown, in one embodiment, the pulse syndrome differentiation method of the present invention includes steps S11 to S15.

[0056] Step S11, collect pulse data and obtain the characteristic parameters of the pulse data.

[0057] The present invention implements dynamic pressure application through a hybrid pressure sensor array to collect pulse data. Specifically, a multi-sensor-based automatic pressure-applying pulse signal acquisition method is adopted, and the pulse signals at the cun, guan, and chi positions are measured simultaneously by three sensors, including control mechanisms such as a stepper motor driver, a stepper motor, an MCU, and a computer. Three high-precision stepper motors are used to drive 3 hybrid pressure sensors to dynamically and accurately apply pressure at the cun, guan, and chi positions of the human body respectively.

[0058] Furthermore, an intelligent "pulse-seeking algorithm" is adopted to simulate the three-finger pulse-taking technique of classic traditional Chinese medicine, obtaining high-precision data feedback. The "pulse-seeking algorithm" refers to the whole process in which the MCU controls the stepping motor (pressure sensor) to take the pulse, with the "pressure" changing from light to medium to heavy, or from heavy to medium to light, or adding left and right pushing and seeking. After finding the most obvious pulsation site, it waits quietly at this place until the final pulse pattern analysis and judgment to determine what kind of pulse condition it belongs to.

[0059] Step S111: The "pulse-seeking algorithm" can set and adjust the reference parameter x based on big data learning and training.

[0060] Step S112: "Superficial pulse-taking of the pulse-seeking algorithm": The displacement step length of the force feedback of the pressure sensor "touching the skin" is i - x.

[0061] Step S113: "Deep pulse-taking of the pulse-seeking algorithm": The displacement step length of the force feedback of the pressure sensor "pushing the tendon against the bone" is j - x.

[0062] Step S114: "Medium pulse-taking of the pulse-seeking algorithm": The displacement step length of the force feedback of the pressure sensor between "superficial pulse-taking" and "deep pulse-taking" is 0.5(j - i - x).

[0063] Step S115: For the six pulses on the left and right hands, steps S111 - S114 are executed in sequence until the end.

[0064] The flexible actuator of the pulse detector is driven to obtain the pulse condition data at the front part. Then, by analyzing the mathematical relationship between the contact position of the sensor and the pressure in the inner cavity of the flexible actuator, the air pressure entering the inner cavity of the flexible actuator is adjusted by means of the solenoid valve to complete the movement of the contact of the pulse detector. Then, the position, number, shape, tendency of the pulse condition and various characteristic parameters of the pulse pattern are automatically recorded and analyzed, which can be used for real-time display of the real-time simulation of the pulse graph during pulse condition detection. The "pulse-seeking algorithm" simulates the pulse-taking method of pulse diagnosis experts, judges through the pressure pulse feeling and pulse condition, and then statistically analyzes and interprets the data of the displacement sensor and the pulse pressure sensor with the help of a specific algorithm by the data processing unit, and maximally restores or simulates the traditional Chinese medicine pulse-taking with the help of high technology, reducing the error caused by the equipment. Thus, the standardization, digitization and objectification of the pulse diagnosis process are completed.

[0065] Among them, the data items for collecting pulse condition data include three categories: patient information, pulse condition analysis and image data. Pulse condition analysis includes pulse name, pulse position, pulse number, pulse force, pulse shape, pulse tendency, pulse rhythm; the image data records the waveform changes and their values under different pressures, serving as the basis for pulse condition analysis.

[0066] Among them, the characteristic parameters of the pulse condition data include: main wave height, tidal wave height, dicrotic notch height, dicrotic wave height, peak maximum value of the first low-pressure area, peak maximum value of the second low-pressure area, time value from the starting point of the pulse wave diagram to the main wave peak point, time value from the starting point of the pulse wave diagram to the dicrotic notch, time value from the dicrotic notch to the ending point of the pulse wave diagram, time value from the starting point to the ending point of the pulse wave diagram, first main wave width, second main wave width, mean pulse rate, pulse rate stability, pulse rate variability, mean of average pulse cycle differences, stability coefficient of average pulse cycle differences, initial pressure value of the pressure sensor probe, maximum downward pressure value of the pressure sensor probe, curvature of the main wave peak. The specific explanations are as follows:

[0067] (1) Main wave height (main wave amplitude) h1;

[0068] (2) Tidal wave height h3;

[0069] (3) Dicrotic notch height h4;

[0070] (4) Dicrotic wave height h5;

[0071] (5) Peak maximum value of the first low-pressure area (the latter 1 / 4 area of the waveform): h6;

[0072] (6) Peak maximum value of the second low-pressure area (the latter 1 / 2 area of the waveform): h7;

[0073] (7) Time value from the starting point of the pulse wave diagram to the main wave peak point. t1 corresponds to the rapid ejection period of the left ventricle: t1;

[0074] (8) Time value from the starting point of the pulse wave diagram to the dicrotic notch. t4 corresponds to the systolic period of the left ventricle: t4;

[0075] (9) Time value from the dicrotic notch to the ending point of the pulse wave diagram. t5 corresponds to the diastolic period of the left ventricle: t5;

[0076] (10) Time value from the starting point to the ending point of the pulse wave diagram. t corresponds to one cardiac cycle of the left ventricle, corresponding to the pulse, that is, one pulsation cycle: t;

[0077] (11) First main wave width: the width of the main wave from the top down by 1 / 3, equivalent to the time maintained by the high pressure level in the artery: w;

[0078] (12) Second main wave width: take the width of the high-pressure area at 2 / 3 height from the top of the main wave: w1;

[0079] (13) Mean pulse rate. Pulse rate is the rate of pulse beats: P. The sum of the pulse rates measured at the six positions of cun, guan, and chi on both sides is divided by 6 to obtain the mean value, which is the mean pulse rate: PR;

[0080] (14) Pulse rate steady state: Subtract the mean pulse rate from the pulse rate, and then divide by the mean pulse rate to obtain the pulse rate steady state. The more stable the pulse rate, the better the compliance function of the heart and arteries, and the more stable the body's stress system. The pulse rate steady state is the best monitoring indicator for reflecting the fire factor: SPR;

[0081] (15) Pulse rate variability, which is obtained by subtracting the minimum pulse rate value from the maximum pulse rate value and then dividing by the mean pulse rate. Pulse rate variability is an indicator for reflecting blood and lymphatic circulation disorders: VPR;

[0082] (16) Mean difference of average pulse cycles of each pulse, which reflects the stability of peripheral blood or lymphatic circulation and the stability of organs other than the heart: Mean D

[0083] (17) The average pulse cycle difference of each pulse, divided by the mean difference of 6 average pulse cycles, reflects the stability coefficient of the average pulse cycle difference. The more stable it is, the more stable the peripheral blood or lymphatic circulation and the more stable the state of organs other than the heart: D stability coefficient;

[0084] (18) Initial pressure value of the pressure sensor probe: Pmin;

[0085] (19) Maximum downward pressure value of the pressure sensor probe: Pmax;

[0086] (20) Curvature of the main wave peak: MR;

[0087] Step S12: Based on the pulse quantification standard, fit the pulse data and establish a pulse characteristic value set database.

[0088] Specifically, divide the pulse data into 27 pulse types based on the pulse quantification standard, analyze each pulse type, and obtain the corresponding characteristic value set to establish a pulse characteristic value set database.

[0089] The pulse characteristic value set database stores the set of characteristic values after processing 27 pulse waveforms. According to the 27 pulse conclusions obtained by experts during pulse diagnosis, combined with the descriptions of the Binhuy Pulse Method and the theory of latent pathology, analyze the waveform diagrams corresponding to each pulse, parse out the relevant characteristic values, and then perform data fitting. Obtain the set of characteristic values corresponding to each pulse. For example, for the long pulse, define the value ranges of characteristic values such as the pulse cycle, pulse rate, rise time T1, fall middle notch time T4, and main wave height H1, etc., as the comparison basis and standard knowledge base for pulse diagnosis and discrimination.

[0090] According to "Binhu Pulse Learning", the 27 common pulse conditions are floating pulse, sinking pulse, slow pulse, rapid pulse, slippery pulse, unsmooth pulse, deficient pulse, excess pulse, long pulse, short pulse, full pulse, minute pulse, taut pulse, moderate pulse, hollow pulse, stringy pulse, leathery pulse, firm pulse, soft pulse, weak pulse, scattered pulse, thin pulse, hidden pulse, thready pulse, intermittent pulse, knotted pulse, and substituting pulse. Each type reflects different physiological states and disease tendencies. For the standard of the measurement parameters of the pressure sensor for specific pulse types, refer to the Shanghai enterprise standard Q31 / 0104000163C001-2019 "Parameter Standard for the Measurement of Traditional Chinese Medicine Pulse by Pressure Sensor".

[0091] The pulse unit is the basic unit for analyzing the pulse condition, which includes the rate, rhythm, intensity and fluency of the pulse beat, as well as the length, width and nature of the pulse body. These parameters together determine the shape and characteristics of the pulse condition. Specifically, it includes:

[0092] Rate: That is, the rate and rhythm of the pulse, which reflect the frequency and regularity of the heart beat.

[0093] Force: It includes the internal and external intensity of the pulse beat and the fluency of the pulse movement, reflecting the fullness and elasticity of the pulse.

[0094] Pulse body: It is composed of length (long or short), width (thick or thin) and nature (hard or soft), describing the overall shape and texture of the pulse.

[0095] By performing intelligent analysis on the pulse waveform collected by the pressure sensor and the pulse waveforms of patients collected over a long period, the characteristic values of pulse units such as stringy, soft, slippery, unsmooth, oblique flying, scattered, broken point, and stagnation can be obtained. These characteristic values reflect the specific attributes of the pulse and are helpful for doctors to make diagnoses. For example, when measuring the pulse unit, stringy, soft, slippery, unsmooth, oblique flying, scattered, broken point, and stagnation are obtained by performing intelligent analysis on the waveform collected by the pressure sensor and the pulse waveforms of patients collected over a long period; rapid, slow, full, thin, long, short, and dry are obtained by comparing the pulse frequency collected by the pressure sensor, the sensing range of the matrix sensor with the set values.

[0096] For each pulse condition type, key characteristic values are extracted through data analysis techniques, and these characteristic values can accurately describe the characteristics of the corresponding pulse condition. For example, for the floating pulse, the time to reach the peak, the rate of pressure change, etc. can be measured. These characteristic values constitute the set of characteristic values for this pulse condition type. The sets of characteristic values for all pulse condition types are integrated into a database to form a database of pulse condition characteristic values. This database not only contains the characteristic values of each pulse condition type but also records important information such as the value range and normal reference values of each characteristic value. The structural design of the database facilitates data storage, retrieval, and update. In practical applications, when a new pulse signal is input into the system, the system can quickly and accurately determine the corresponding pulse condition type by analyzing the characteristic values of the signal and comparing them with the set of characteristic values stored in the database. This has important application value for clinical diagnosis, disease prediction, and evaluation of treatment effects, etc.

[0097] Through the above steps, the establishment of the database of pulse condition characteristic values based on the pulse quantification standard not only promotes the standardization and modernization of traditional Chinese medicine pulse diagnosis technology but also provides a solid foundation for the objective and scientific research of traditional Chinese medicine. In addition, the application of this method also makes it possible for the global dissemination and increased acceptance of traditional Chinese medicine because it provides a more intuitive and standardized way to explain and teach pulse diagnosis knowledge.

[0098] Step S13: Construct and train a pulse quantification mathematical model based on the database of pulse condition characteristic values.

[0099] Constructing and training a pulse quantification mathematical model combines the traditional pulse diagnosis of traditional Chinese medicine with modern technology, and the specific steps are as follows:

[0100] Step S131: Data preprocessing.

[0101] Use a three-probe piezoresistive sensor and self-inductive pressure technology to collect pulse data. These sensors can capture parameters such as the waveform, rate, rhythm, and intensity of the pulse. Filter and denoise the collected original signal to ensure data quality. Extract key features from the processed signal, such as the height, width, area of the waveform, and the rhythm and intensity of the pulse.

[0102] Step S132: Construct a pulse quantification mathematical model.

[0103] Select a suitable machine learning model, such as a deep learning neural network, especially a convolutional neural network (CNN) or a recurrent neural network (RNN). These models are good at processing sequential data and extracting patterns from it. Use a large amount of labeled clinical data to train the model. Through training, the model learns how to predict the pulse condition type based on the input pulse waveform and other parameters.

[0104] Step S133: Automatic judgment and program scheduling.

[0105] Automatically analyze the characteristics of the pulse waveform through the pulse condition judgment matrix algorithm, and compare it with the standardized pulse conditions in the database to achieve automatic identification and classification of pulse conditions. Integrate it into the clinical workflow to ensure that the model can receive new pulse data in real time and provide analysis results quickly and accurately.

[0106] Step S134: Construct the knowledge graph association.

[0107] Apply data mining techniques such as clustering, association rules, and decision trees to extract and summarize traditional Chinese medicine syndrome types and corresponding treatment prescriptions from historical cases. Based on the mining results, construct a pulse syndrome (disease)-prescription knowledge graph containing the relationships of "disease-syndrome-prescription". This graph can help doctors quickly find appropriate treatment plans.

[0108] That is, use data mining techniques to extract and summarize traditional Chinese medicine syndrome types from complex symptoms, and analyze the potential association rules between symptoms and symptoms, symptoms and prescriptions, symptoms and syndrome types, syndrome types and prescriptions, prescriptions and prescriptions, etc. Discovering the drug compatibility rules, potential drugs, core drugs, core prescriptions, etc. from a large number of prescriptions can not only provide clinical treatment strategies, but also simulate the traditional Chinese medicine thinking method and prescription generation process.

[0109] Step S135: Integrate and optimize.

[0110] The pulse condition dialectic method described in the present invention further includes a feedback mechanism. Based on this feedback mechanism, doctors are allowed to update the database and the model according to the actual treatment effect, continuously improving the accuracy and reliability of the model. By cooperating with traditional Chinese medicine experts, ensure that the model and algorithm conform to traditional Chinese medicine theory and clinical practice, and enhance the practicality and acceptance of the model. Regularly evaluate the performance of the model, including indicators such as accuracy, recall rate, and precision, to ensure that it continuously meets clinical needs.

[0111] Through these steps, a powerful mathematical model for quantifying pulse conditions can be constructed. This model can not only improve the accuracy and efficiency of pulse diagnosis, but also deepen the integration of traditional Chinese medicine and modern technology, and promote the modernization and internationalization development of traditional Chinese medicine.

[0112] Among them, the pulse condition judgment matrix algorithm refers to judging whether each index in the pulse condition meets the preset conditions, and judging that the index is qualified (or unqualified) when the preset conditions are met. The pulse condition judgment matrix algorithm described in step S133 is as follows:

[0113] / / 1. Floating pulse

[0114] Case

[0115] Parameter standard pressure value: Pmin×(1.01~1.02)

[0116] and the complete waveform (each wave covers at least the technical parameters h1, t) >= 3

[0117] Then Print “Float”;

[0118] / / 2. Sinking Pulse

[0119] Case

[0120] The standard pressure value of the parameter: Pmax × (0.99 - 0.98)

[0121] and the complete waveform (each wave covers at least the technical parameters h1, t) >= 3

[0122] Then Print “Sinking”;

[0123] / / 3. Slow Pulse

[0124] Case P ≤ 60 beats / min, -5% ≤ SPR ≤ 5%, VPR ≤ 5%, D average ≤ 12, and D stability coefficient ≤ 1,

[0125] Then Print “Slow”;

[0126] / / 4. Rapid Pulse

[0127] Case

[0128] The parameter standard is P >= 90 beats / min, -5% ≤ SPR ≤ 5%, VPR ≤ 5%, D average ≤ 12, and D stability coefficient ≤ 1,

[0129] Then Print “Rapid”;

[0130] / / 5. Slippery Pulse

[0131] Case h1 ≥ 50g, and D average ≥ 36, and D stability coefficient ≥ 4;

[0132] OR h1 ≥ 50g, and w / t > 0.35;

[0133] OR h1 ≥ 50g, and h7 / h1 < 0.1;

[0134] Then Print “Slippery”;

[0135] / / 6. Stringy Pulse

[0136] Case

[0137] 50g < h1 < 100g, 15% < VPR ≤ 30% And h4 / h1 ≥ 0.65;

[0138] OR

[0139] 50 g < h1 < 100 g, 15% < VPR ≤ 30% And w / t ≤ 0.15;

[0140] OR

[0141] 50 g < h1 < 100 g, 15% < VPR ≤ 30% And -30% < SPR ≤ -20%; OR

[0142] 50 g < h1 < 100 g, 15% < VPR ≤ 30% And 20% ≤ SPR < 30%;

[0143] Then Print “Astringent”;

[0144] / / 7. Deficient Pulse

[0145] Case

[0146] Parameter standard pressure value: Pmax × (0.99 - 0.98) and complete waveform (each wave covers at least technical parameters h1, t) < 3

[0147] And

[0148] Parameter standard pressure value: Pmin × (1.01 - 1.02) and complete waveform (each wave covers at least technical parameters h1, t) >= 3 And

[0149] P ≤ 60 beats / min, -5% ≤ SPR ≤ 5%, VPR ≤ 5%, D mean ≤ 12, and D stability coefficient ≤ 1

[0150] Then Print “Deficient”;

[0151] / / 8. Excess Pulse

[0152] Case

[0153] (Parameter standard pressure value: Pmin × (1.01 - 1.02) and complete waveform (each wave covers at least technical parameters h1, t) >= 3 and

[0154] Parameter standard pressure value: Pmax × (0.99 - 0.98) and complete waveform (each wave covers at least technical parameters h1, t) >= 3 and

[0155] h1 ≥ 50 g, MR < 0.015 OR MR > 0.05, D mean ≥ 36, D stability coefficient ≥ 4 and

[0156] t > 300 milliseconds, (h6 + h7) / h1 ≥ 1; )

[0158] OR

[0159] (Parameter standard pressure value: Pmin×(1.01~1.02) and complete waveform (each wave at least covers technical parameters h1, t)>=3and

[0160] Parameter standard pressure value: Pmax×(0.99~0.98)and complete waveform (each wave at least covers technical parameters h1, t)>=3and

[0161] h1≥50g, MR<0.015OR MR>0.05, D mean ≥36, D stability coefficient ≥4and

[0162] t>300 ms and w / t<0.15. )

[0164] Then Print "real";

[0165] / / 9. Long pulse

[0166] Case

[0167] t>300 ms, (h6+h7) / h1≥1;

[0168] OR t>300 milliseconds, and w / t<0.15;

[0169] Then Print "long";

[0170] / / 10. Short pulse

[0171] Case t≤300ms and D stability coefficient<4and MR<0.01;

[0172] OR t≤300 ms and D stability factor <4 and t1≤40 ms.

[0173] Then Print "short";

[0174] / / 11. Hongmai

[0175] Case h1>100g, h4 / h1>0.65.

[0176] Then Print "Hong";

[0177] / / 12. Micro Pulse

[0178] Case

[0179] (D mean ≥ 36, pulse rate steady state ≤ -30%, or pulse rate steady state ≥ 30%.

[0180] And h5≠0, h1<50g, w / t≤0.2.

[0181] And h1<50g, h1 / t1≥1. )OR

[0182] (D mean ≥ 36, pulse rate steady state ≤ -30%, or pulse rate steady state ≥ 30%.

[0183] And h5≠0, h1<50g, does not meet w / t≤0.2, but 12<D average<36. And h1<50g, h1 / t1≥1. )

[0185] Then Print "微";

[0186] / / 13. Tight pulse

[0187] Case

[0188] (h5=0,w / t>0.2。And pulse rate variability>30%)

[0189] OR

[0190] (h5=0,w / t>0.2。And pulse rate steady state ≤-30%, or pulse rate steady state ≥30%.)Then Print “tight”;

[0191] / / 14. Slow pulse

[0192] Case (

[0194] The parameters of the wave are consistent with a pulse rate of 60 to 90 beats / min and

[0195] When t≥120000 milliseconds (2 minutes), |t is the number of waves within 0 to 60000 milliseconds - t is the number of waves within 60000 milliseconds to 120000 milliseconds |≤2and

[0196] h1≥50g, D mean≥36, and D stability coefficient≥4;

[0197] )OR (

[0199] The parameters of the wave are consistent with a pulse rate of 60 to 90 beats / min and

[0200] When t≥120000 milliseconds (2 minutes), |t is the number of waves within 0 to 60000 milliseconds - t is the number of waves within 60000 milliseconds to 120000 milliseconds |≤2and

[0201] h1 ≥ 50g, does not meet the requirements of D average ≥ 36 and D stability coefficient ≥ 4, but w / t > 0.35;

[0202] )OR (

[0204] The parameters of the wave meet the requirements that the pulse rate is 60 - 90 beats per minute and

[0205] when t ≥ 120000 milliseconds (2 minutes), |the number of wave values within 0 - 60000 milliseconds of t - the number of wave values within 60000 milliseconds - 120000 milliseconds of t| ≤ 2 and

[0206] h1 ≥ 50g, does not meet the requirements of D average ≥ 36 and D stability coefficient ≥ 4, nor does it meet w / t > 0.35, but h7 / h1 < 0.1. )

[0208] Then Print “Slow”;

[0209] / / 15. Floating and Empty Pulse

[0210] Case (

[0212] The pressure value is the initial pressure value of the probe × (1.01 - 1.02), and more than 3 complete waveforms can be continuously measured

[0213] shapes (each wave covers at least the technical parameters h1, t) and

[0214] The pressure value is [(the initial pressure value of the probe × (1.01 - 1.02) + the maximum downward pressure value of the probe × (0.99 -

[0215] 0.98)] / 2, and more than 3 complete waveforms cannot be continuously measured (each wave covers at least the technical parameters h1, t) and the pressure value is the maximum downward pressure value of the probe × (0.99 - 0.98), and more than 3 complete

[0216] waveforms can be continuously measured (each wave covers at least the technical parameters h1, t). )

[0218] Then Print “Floating and Empty”;

[0219] / / 16. Taut Pulse

[0220] Case

[0221] h5 = 0, if w / t > 0.2, and 0 < h4 / h1 < 0.65;

[0222] OR

[0223] h5 = 0, if w / t > 0.2, it does not meet 0 < h4 / h1 < 0.65, but 50g < h1 < 100g.

[0224] Then Print “string”;

[0225] / / 17. Severe Pulse

[0226] Case (

[0228] The pressure value is the initial pressure value of the probe × (1.01 - 1.02), and more than 3 complete waveforms can be continuously measured (each wave covers at least the technical parameters h1, t) and

[0229] The pressure value is the maximum downward pressure value of the probe × (0.99 - 0.98), and more than 3 complete waveforms can be continuously measured (each wave covers at least the technical parameters h1, t) and

[0230] The pressure value is [the initial pressure value of the probe × (1.01 - 1.02) + the maximum downward pressure value of the probe × (0.99 - 0.98)] / 2, and more than 3 complete waveforms cannot be continuously measured (each wave covers at least the technical parameters h1, t) and

[0231] Condition 4: h5 = 0, if w / t > 0.2, and 0 < h4 / h1 < 0.65;

[0232] ) OR (

[0234] The pressure value is the initial pressure value of the probe × (1.01 - 1.02), and more than 3 complete waveforms can be continuously measured (each wave covers at least the technical parameters h1, t) and

[0235] The pressure value is the maximum downward pressure value of the probe × (0.99 - 0.98), and more than 3 complete waveforms can be continuously measured (each wave covers at least the technical parameters h1, t) and

[0236] The pressure value is [the initial pressure value of the probe × (1.01 - 1.02) + the maximum downward pressure value of the probe × (0.99 - 0.98)] / 2, and more than 3 complete waveforms cannot be continuously measured (each wave covers at least the technical parameters h1, t) and

[0237] h5 = 0, if w / t > 0.2, it does not meet 0 < h4 / h1 < 0.65, but 50g < h1 < 100g )

[0239] Then Print “severe”;

[0240] / / 18. Firm Pulse

[0241] Case (

[0243] The pressure value is the initial pressure value of the probe × (1.01 - 1.02), and more than 3 complete waveforms cannot be continuously measured (each wave should cover at least the technical parameters h1, t) and

[0244] The pressure value is the maximum downward pressure value of the probe × (0.99 - 0.98), and more than 3 complete waveforms can be continuously measured (each wave should cover at least the technical parameters h1, t) and

[0245] h5 = 0, if w / t > 0.2, and 0 < h4 / h1 < 0.65;

[0246] ) OR

[0247] The pressure value is the initial pressure value of the probe × (1.01 - 1.02), and more than 3 complete waveforms cannot be continuously measured (each wave should cover at least the technical parameters h1, t) and

[0248] The pressure value is the maximum downward pressure value of the probe × (0.99 - 0.98), and more than 3 complete waveforms can be continuously measured (each wave should cover at least the technical parameters h1, t) and

[0249] h5 = 0, if w / t > 0.2, 0 < h4 / h1 < 0.65 does not meet the requirement, but 50g < h1 < 100g

[0250] ) OR (

[0252] The pressure value is the initial pressure value of the probe × (1.01 - 1.02), and more than 3 complete waveforms cannot be continuously measured (each wave should cover at least the technical parameters h1, t) and

[0253] The pressure value is the maximum downward pressure value of the probe × (0.99 - 0.98), and more than 3 complete waveforms can be continuously measured (each wave should cover at least the technical parameters h1, t) and

[0254] t > 300 milliseconds, (h6 + h7) / h1 ≥ 1;

[0255] ) OR (

[0257] The pressure value is the initial pressure value of the probe × (1.01 - 1.02), and more than 3 complete waveforms cannot be continuously measured (each wave covers at least the technical parameters h1, t) and

[0258] The pressure value is the maximum downward pressure value of the probe × (0.99 - 0.98), and more than 3 complete waveforms can be continuously measured (each wave covers at least the technical parameters h1, t) And

[0259] t > 300 milliseconds, does not meet (h6 + h7) / h1 ≥ 1, but w / t < 0.15. )

[0261] Then Print “Lao”;

[0262] / / 19. Floating and Weak Pulse

[0263] Case (

[0265] h5 ≠ 0, h1 < 50g, w / t ≤ 0.2 and

[0266] h1 < 50g, h1 / t1 ≥ 1 and

[0267] The pressure value is the initial pressure value of the probe × (1.01 - 1.02), and more than 3 complete waveforms are continuously measured (each wave covers at least the technical parameters h1, t).

[0268] ) OR (

[0270] h5 ≠ 0, h1 < 50g, does not meet w / t ≤ 0.2, but 12 < average value of D < 36 and

[0271] h1 < 50g, h1 / t1 ≥ 1 and

[0272] The pressure value is the initial pressure value of the probe × (1.01 - 1.02), and more than 3 complete waveforms are continuously measured (each wave covers at least the technical parameters h1, t). )

[0274] Then Print “Ru”;

[0275] / / 20. Weak Pulse

[0276] Case (

[0278] The pressure value is the initial pressure value of the probe × (1.01 - 1.02), and more than 3 complete waveforms cannot be continuously measured (each wave covers at least the technical parameters h1, t) And

[0279] The pressure value is the maximum downward pressure value of the probe × (0.99 - 0.98), and more than 3 complete waveforms can be continuously measured, and the parameters of the wave meet h1 < 50g, h1 / t1 ≥ 1 and

[0280] The pressure value is [the initial pressure value of the probe × (1.01 - 1.02) + the maximum downward pressure value of the probe × (0.99 - 0.98)] / 2, and more than 3 complete waveforms cannot be continuously measured. And condition 4: h5 ≠ 0, h1 < 50g, w / t ≤ 0.2; and

[0281] h1 < 50g, h1 / t1 ≥ 1.

[0282] ) OR (

[0284] The pressure value is the initial pressure value of the probe × (1.01 - 1.02), and more than 3 complete waveforms cannot be continuously measured (each wave covers at least the technical parameters h1, t) And

[0285] The pressure value is the maximum downward pressure value of the probe × (0.99 - 0.98), and more than 3 complete waveforms can be continuously measured, and the parameters of the wave meet h1 < 50g, h1 / t1 ≥ 1 and

[0286] The pressure value is [the initial pressure value of the probe × (1.01 - 1.02) + the maximum downward pressure value of the probe × (0.99 - 0.98)] / 2, and more than 3 complete waveforms cannot be continuously measured And

[0287] h5 ≠ 0, h1 < 50g, does not meet w / t ≤ 0.2, but 12 < average D < 36; and

[0288] h1 < 50g, h1 / t1 ≥ 1. )

[0290] Then Print “Weak”;

[0291] / / 21. Scattered pulse

[0292] Case (

[0294] The pressure value is the initial pressure value of the probe × (1.01 - 1.02), and more than 3 complete waveforms can be continuously measured (each wave covers at least the technical parameters h1, t) and

[0295] The pressure value is the maximum downward pressure value of the probe × (0.99 - 0.98), and more than 3 complete waveforms cannot be continuously measured, and the parameters of the wave meet h1 < 50g, h1 / t1 ≥ 1 and

[0296] The pressure value is [probe initial pressure value × (1.01 - 1.02) + probe maximum downward pressure value × (0.99 - 0.98)] / 2, and more than 3 complete waveforms cannot be continuously measured and

[0297] h1 ≤ 30g, h4 / h1 > 0.65;

[0298] )or (

[0300] The pressure value is probe initial pressure value × (1.01 - 1.02), and more than 3 complete waveforms can be continuously measured (each wave covers at least technical parameters h1, t) and

[0301] The pressure value is probe maximum downward pressure value × (0.99 - 0.98), more than 3 complete waveforms cannot be continuously measured, and the parameters of the wave meet h1 < 50g, h1 / t1 ≥ 1 and

[0302] Condition 3: The pressure value is [probe initial pressure value × (1.01 - 1.02) + probe maximum downward pressure value × (0.99 - 0.98)] / 2, and more than 3 complete waveforms cannot be continuously measured and

[0303] h1 ≤ 30g, h4 / h1 > 0.65 is not met, but h6 / h1 > 0.9.

[0304] )OR (

[0306] The pressure value is probe initial pressure value × (1.01 - 1.02), and more than 3 complete waveforms can be continuously measured (each wave covers at least technical parameters h1, t) and

[0307] The pressure value is probe maximum downward pressure value × (0.99 - 0.98), more than 3 complete waveforms cannot be continuously measured, and the parameters of the wave meet h1 < 50g, h1 / t1 ≥ 1 and

[0308] The pressure value is [probe initial pressure value × (1.01 - 1.02) + probe maximum downward pressure value × (0.99 - 0.98)] / 2, and more than 3 complete waveforms cannot be continuously measured and

[0309] h1 ≤ 30g, the pulse rate variability is greater than 30% )

[0311] Then Print “scattered”;

[0312] / / 22. Thready pulse

[0313] Case

[0314] h1 < 50 g, h1 / t1 ≥ 1.

[0315] Then Print “Thin”;

[0316] / / 23. Deep Pulse

[0317] Case (

[0319] The pressure value is the initial pressure value of the probe × (1.01 - 1.02), and more than 3 complete waveforms cannot be continuously measured (each wave should at least cover the technical parameters h1, t) and

[0320] The pressure value is the maximum downward pressure value of the probe × (0.99 - 0.98), and more than 3 complete waveforms can be continuously measured (each wave should at least cover the technical parameters h1, t) and

[0321] The pressure value is [the initial pressure value of the probe × (1.01 - 1.02) + the maximum downward pressure value of the probe × (0.99 - 0.98)] / 2, and more than 3 complete waveforms cannot be continuously measured (each wave should at least cover the technical parameters h1, t))

[0322] Then Print “Deep”;

[0323] / / 24. Artery

[0324] Case (

[0326] t ≤ 300 milliseconds, D stability coefficient < 4, main peak curvature < 0.01;

[0327] And the pulse rate is greater than 90 beats per minute.

[0328] ) OR

[0329] t ≤ 300 milliseconds, D stability coefficient < 4, does not meet the main peak curvature < 0.01, but t1 ≤ 40 milliseconds.

[0330] And the pulse rate is greater than 90 beats per minute. )

[0332] Then Print “Artery”;

[0333] / / 25. Rapid Pulse

[0334] Case (

[0336] The pulse rate is greater than 90 beats per minute, -20% < pulse rate steady state < -5%, or 5% < pulse rate steady state < 20%.

[0337] And

[0338] When t ≥ 120000 milliseconds (2 minutes), |the number of waves in the range of 0 to 60000 milliseconds of t - the number of waves in the range of 60000 milliseconds to 120000 milliseconds of t| > 2.

[0339] )or (

[0341] The pulse rate is greater than 90 beats per minute, and 5% < pulse rate variability ≤ 15%.

[0342] And

[0343] When t ≥ 120000 milliseconds (2 minutes), |the number of waves in the range of 0 to 60000 milliseconds of t - the number of waves in the range of 60000 milliseconds to 120000 milliseconds of t| > 2.

[0344] )or (

[0346] The pulse rate is greater than 90 beats per minute, and 12 < average D < 36.

[0347] And

[0348] When t ≥ 120000 milliseconds (2 minutes), |the number of waves in the range of 0 to 60000 milliseconds of t - the number of waves in the range of 60000 milliseconds to 120000 milliseconds of t| > 2.

[0349] )or (

[0351] The pulse rate is greater than 90 beats per minute, and 1 < D stability coefficient < 4.

[0352] And

[0353] When t ≥ 120000 milliseconds (2 minutes), |the number of waves in the range of 0 to 60000 milliseconds of t - the number of waves in the range of 60000 milliseconds to 120000 milliseconds of t| > 2. )

[0355] Then Print “Urgent”;

[0356] / / 26. Jie Pulse

[0357] Case (

[0359] The pulse rate is less than 60 beats per minute, and -5% ≤ pulse rate steady state ≤ 5%.

[0360] And

[0361] When t ≥ 120000 milliseconds (2 minutes), |the number of waves in the range of 0 to 60000 milliseconds of t - the number of waves in the range of 60000 milliseconds to 120000 milliseconds of t| > 2

[0362] ) or (

[0364] The pulse rate is less than 60 beats per minute, and the pulse rate variability is ≤ 5%.

[0365] And

[0366] When t ≥ 120,000 milliseconds (2 minutes), |the number of wave values of t within 0 - 60,000 milliseconds - the number of wave values of t within 60,000 milliseconds - 120,000 milliseconds| > 2.

[0367] ) or (

[0369] The pulse rate is less than 60 beats per minute, and the D mean ≤ 12.

[0370] And

[0371] When t ≥ 120,000 milliseconds (2 minutes), |the number of wave values of t within 0 - 60,000 milliseconds - the number of wave values of t within 60,000 milliseconds - 120,000 milliseconds| > 2.

[0372] ) or (

[0374] The pulse rate is less than 60 beats per minute, and the D stability coefficient ≤ 1.

[0375] And

[0376] When t ≥ 120,000 milliseconds (2 minutes), |the number of wave values of t within 0 - 60,000 milliseconds - the number of wave values of t within 60,000 milliseconds - 120,000 milliseconds| > 2. )

[0378] Then Print “Jie”;

[0379] / / 27. Substitute pulse

[0380] Case (

[0382] The steady - state pulse rate is greater than or equal to - 5% and less than or equal to 5% (i.e., - 5% ≤ SPR ≤ 5%).

[0383] And

[0384] The pulse rate variability is less than or equal to 5%.

[0385] And

[0386] The D mean ≥ 36.

[0387] And

[0388] When t≥120000 milliseconds (2 minutes), |the number of waves of t within 0 to 60000 milliseconds - the number of waves of t within 60000 milliseconds to 120000 milliseconds| ≤ 2.

[0389] )or

[0390] The steady state of the pulse rate is greater than or equal to -5% and less than or equal to 5% (i.e., -5% ≤ SPR ≤ 5%).

[0391] And

[0392] The pulse rate variability is less than or equal to 5%.

[0393] And

[0394] The D stability coefficient ≥ 4.

[0395] And

[0396] When t≥120000 milliseconds (2 minutes), |the number of waves of t within 0 to 60000 milliseconds - the number of waves of t within 60000 milliseconds to 120000 milliseconds| ≤ 2. )

[0398] Then Print “Dai”;

[0399] Step S14: Obtain the pulse waveform diagram of the person to be diagnosed.

[0400] Step S15: Input the pulse waveform diagram of the person to be diagnosed into the pulse quantification mathematical model to obtain the pulse diagnosis result.

[0401] Specifically, inputting the pulse waveform diagram of the person to be diagnosed into the pulse quantification mathematical model to obtain the pulse diagnosis result includes the following steps:

[0402] Analyze the pulse waveform diagram of the person to be diagnosed by wavelet analysis method to obtain the characteristic parameters of the pulse data of the person to be diagnosed;

[0403] Input the characteristic parameters of the pulse data of the person to be diagnosed into the pulse quantification mathematical model to obtain the corresponding pulse type;

[0404] Combine the clinical information and analyze and judge the pulse type to generate a comprehensive decision of pulse and syndrome.

[0405] After inputting the pulse waveform diagram of the person to be diagnosed into the pulse quantification mathematical model to obtain the pulse diagnosis result, it further includes generating a treatment plan based on the pulse diagnosis result.

[0406] As Figure 2 shown, Figure 2 It shows the core flowchart of the pulse quantification and judgment algorithm described in the present invention. The specific pulse data processing flow is as follows:

[0407] Step S151, data preparation and input stage.

[0408] Including data for pulse quantification, pulse quantification / judgment matrix, and pulse feature value set database;

[0409] Step S152, data processing and analysis stage.

[0410] Use the pulse detector probe to detect the new waveform and perform A / D conversion. Pulse pressure waveform conversion algorithm: According to the analog pulse wave pressure signal detected by the device probe, after A / D conversion, a digital signal is generated, which is convenient for further analysis and processing of the pulse wave digital signal feature values, feature value sets, data ranges, etc.

[0411] Process the characteristic parameters obtained by the pulse pressure waveform conversion algorithm through the waveform analysis algorithm. According to the waveform data, important parameters of the waveform are parsed for the next post-processing. The parameters include: pulse period, pulse rate, rise time T1, dicrotic notch time T4, main wave height H1, dicrotic notch height H4, replay wave height H5, main wave peak curvature, etc., a total of 20 parameters.

[0412] Step S153, pulse condition judgment and analysis decision-making stage.

[0413] Perform pulse condition judgment through the pulse condition judgment matrix and the pulse self-learning feature value set database to obtain the six qi pulse data of both hands; then use the pulse operation analysis algorithm to extract the common characteristic parameters of the pulse diagnosis waveform, and finally generate pulse operation data to support the comprehensive decision-making of pulse signs.

[0414] Step S154, diagnosis conclusion output stage.

[0415] Through the six qi pulse data of both hands, combined with the comprehensive decision-making of pulse signs and the analysis results of pulse condition analysis & disease index, the diagnosis conclusion is obtained according to the etiology / pathological analysis. If personalized recommendations are needed, personalized recommendations can be made according to the exercise / diet / physical therapy / product plan.

[0416] Among them, the 27 pulses in step S153 refer to, according to traditional Chinese medicine theory, by palpating different characteristics of the pulse, classifying the pulse conditions into 27 types. These pulse conditions include floating, sinking, slow, rapid, slippery, unsmooth, etc. Each pulse condition reflects different physiological and pathological states of the human body. The six qi pulses refer to, according to the six qi theory of traditional Chinese medicine, classifying the pulse conditions into six types, namely wind, cold, summer heat, dampness, dryness, and fire. These six qi respectively correspond to the climate changes in nature and are also closely related to the visceral functions and pathological changes of the human body. The relationship between the 27 pulses and the six qi pulses is based on the overall concept and the theory of yin-yang and the five elements in traditional Chinese medicine. The 27 pulses are a direct reflection of the physiological and pathological states of the human body, while the six qi pulses are a summary of the relationship between the climate changes in nature and human health. The changes in the 27 pulses can be affected by the six qi pulses. For example, the invasion of cold pathogens into the human body may lead to a sinking and slow pulse condition; conversely, the changes in the six qi pulses will also affect the manifestation of the 27 pulses. For example, hot summer weather may cause the human body to show a floating and rapid pulse condition. In traditional Chinese medicine diagnosis, doctors will comprehensively use the knowledge of the 27 pulses and the six qi pulses to judge the nature, location, and cause of diseases. By observing and analyzing the changes in the patient's pulse condition, doctors can accurately diagnose and treat diseases.

[0417] Through the careful observation and analysis of the 27 pulses, the pulse condition dialectical method described in the present invention can understand the specific condition of the patient, so as to formulate a personalized treatment plan. At the same time, combined with the theory of the six qi pulses, it can also predict the development and changes of diseases and provide more comprehensive health guidance for patients. Therefore, the relationship between the pulse condition and the six qi pulses can be mapped in the pulse condition research and judgment matrix of the present invention. The quantified pulse condition data is applied to this research and judgment matrix, and the relevant data of the six qi pulses are obtained through matrix operations (the six qi pulse condition analysis algorithm).

[0418] As Figure 3 shown, Figure 3 It shows a schematic diagram of a digital device in an embodiment of the pulse condition dialectical method described in the present invention. The pulse condition dialectical device includes a pulse graph data acquisition module 31, a training module 32 based on big data & deep learning, a pulse condition parameter quantification and research and judgment module 33, a pulse diagnosis linkage intelligent prescription module 34, and a pulse diagnosis test report display module 35.

[0419] The pulse graph data acquisition module 31 is used to collect the pulse signals of the cun, guan, and chi positions through a hybrid pressure sensor array; the software part automatically records and analyzes the position, number, shape, momentum of the pulse and the various characteristic parameters of the pulse graph, and can be used for real-time display of the real-time simulation of the pulse graph during pulse condition detection.

[0420] The training module 32 based on big data & deep learning mainly trains and learns through pulse condition big data to construct a quantitative mathematical model of the pulse parameters of the pressure sensor; realizes the automatic research and judgment of the pulse parameters and program scheduling; continuously trains and learns based on the theory of latent diseases and the traditional Chinese medicine expert database system to construct the knowledge graph association of pulse evidence (disease) - prescription.

[0421] The pulse parameter quantization and judgment module 33 is used to perform software-based and knowledge-based operations based on the quantization criteria of module 32. It converts, schedules, and judges the parameter data of the hybrid pressure sensor array into the diagnostic conclusion data of the objectification, standardization, digitization, and consistency of 27 pulses in traditional Chinese medicine.

[0422] The pulse diagnosis linkage intelligent prescription module 34 is used to perform continuous learning and training based on the latent disease theory of module 32 and the traditional Chinese medicine expert database. The pulse diagnosis conclusion (disease diagnosis) can be linked with traditional classical prescriptions, expert prescriptions, patent prescriptions, etc., and different types of prescriptions can be efficiently called with one key, greatly reducing the time cost of prescribing.

[0423] The pulse diagnosis detection report display module 35 mainly provides a comprehensive pulse diagnosis detection report that can be visually displayed to the subjects and operators. It includes: pulse diagnosis data analysis, etiology, pathological analysis, physical therapy, exercise, diet plan, etc.

[0424] In the modernization process of traditional Chinese medicine pulse diagnosis, the technical solution proposed in this application has brought significant technical effects and innovations in many aspects. Specifically as follows:

[0425] (1) Improve the accuracy and objectivity of pulse diagnosis

[0426] By introducing modern sensor technology and signal processing algorithms, this application can accurately collect and analyze pulse waveforms, reduce the subjective errors that may occur in traditional manual pulse diagnosis, and improve the accuracy of pulse diagnosis. Using algorithms such as deep learning to perform quantitative analysis on pulse data makes the pulse diagnosis results more objective and repeatable, and helps to achieve the standardization of traditional Chinese medicine pulse diagnosis.

[0427] (2) Realize the digitization and intelligence of pulse diagnosis data

[0428] This application converts complex pulse information into digital signals, which is convenient for storage, transmission, and analysis, providing a basis for further research and application of pulse diagnosis data. Combining machine learning and artificial intelligence technologies, this application can automatically identify and predict pulse patterns, providing decision-making support for doctors and realizing the intelligence of pulse diagnosis.

[0429] (3) Promote the integration of traditional Chinese medicine theory and modern technology

[0430] Based on respecting and inheriting traditional Chinese medicine theory, this application innovatively applies modern technology to the field of pulse diagnosis, promoting the deep integration of traditional Chinese medicine theory and modern technology. Through actual cases, it verifies the application value of modern sensors and data analysis technology in traditional Chinese medicine pulse diagnosis, demonstrating the potential of traditional Chinese medicine modernization.

[0431] (4) Improve the diagnostic efficiency and patient experience

[0432] The automated pulse diagnosis process reduces the workload of doctors, improves the diagnosis efficiency, and enables doctors to focus more on disease analysis and treatment decisions. The non-invasive pulse diagnosis device reduces patient discomfort, and the friendly user interface and fast diagnosis process enhance the patient's medical experience.

[0433] (5) Promote the internationalization and standardization of traditional Chinese medicine pulse diagnosis

[0434] The technical solution of this application helps to promote traditional Chinese medicine pulse diagnosis to the international market, enabling more people to understand and accept the unique diagnosis and treatment methods of traditional Chinese medicine. By establishing unified pulse diagnosis data standards and analysis processes, this application promotes the development of traditional Chinese medicine pulse diagnosis towards standardization and regularization.

[0435] The protection scope of the pulse condition dialectical method described in the embodiments of the present invention is not limited to the execution order of the steps listed in this embodiment. Any solution achieved by adding or subtracting steps of the prior art and replacing steps according to the principles of the present invention is included in the protection scope of the present invention.

[0436] The embodiments of the present invention also provide a pulse condition dialectical system. The pulse condition dialectical system can implement the pulse condition dialectical method described in the present invention. However, the implementation devices of the pulse condition dialectical system described in the present invention include but are not limited to the structure of the pulse condition dialectical system listed in this embodiment. Any structural deformation and replacement of the prior art made according to the principles of the present invention are included in the protection scope of the present invention.

[0437] As Figure 4 shown, in one embodiment, the pulse condition dialectical system of the present invention includes a collection module 41, a fitting module 42, a quantization module 43, an acquisition module 44, and a dialectical module 45;

[0438] The collection module 41 is used to collect pulse condition data and obtain the characteristic parameters of the pulse condition data;

[0439] The fitting module 42 is connected to the collection module 41. The fitting module 42 is used to fit the pulse condition data based on the pulse condition quantization standard and establish a pulse condition eigenvalue set database;

[0440] The quantization module 43 is connected to the fitting module 42. The quantization module 43 is used to construct and train a pulse condition quantization mathematical model based on the pulse condition eigenvalue set database;

[0441] The acquisition module 44 is connected to the quantization module 43. The acquisition module 44 is used to obtain the pulse waveform diagram of the person to be dialectically diagnosed;

[0442] The dialectical module 45 is connected to the acquisition module 44, and the dialectical module 45 is configured to input the pulse waveform diagram of the person to be dialectically analyzed into the pulse quantization mathematical model to obtain a pulse dialectical result.

[0443] In several embodiments provided by the present invention, it should be understood that the disclosed system, apparatus or method can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of modules / units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the displayed or discussed mutual coupling or direct coupling or communication connection can be through some interfaces, and the indirect coupling or communication connection of the apparatus or module or unit can be in an electrical, mechanical or other form.

[0444] The modules / units described as separate components may or may not be physically separated, and the components shown as modules / units may or may not be physical modules, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules / units can be selected according to actual needs to achieve the purpose of the embodiments of the present invention. For example, in each embodiment of the present invention, the various functional modules / units can be integrated in a processing module, or each module / unit can exist physically alone, or two or more modules / units can be integrated in one module / unit.

[0445] Those of ordinary skill in the art should also further realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of the examples have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0446] An embodiment of the present invention also provides a computer-readable storage medium. Those of ordinary skill in the art can understand that all or part of the steps in the method of the above embodiments can be completed by instructing a processor through a program. The program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center integrating one or more available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a digital video disc (DVD)), or a semiconductor medium (such as a solid-state disk (SSD)), etc.

[0447] An embodiment of the present invention also provides an electronic device. The electronic device includes a processor and a memory.

[0448] The memory is used to store a computer program.

[0449] The memory includes various media that can store program codes, such as ROM, RAM, magnetic disk, USB flash drive, memory card, or optical disc.

[0450] The processor is connected to the memory and is used to execute the computer program stored in the memory, so that the electronic device executes the above pulse condition dialectic method.

[0451] Preferably, the processor can be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it can also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0452] Such as Figure 5As shown, the electronic device of the present invention is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: one or more processors or processing units 51, a memory 52, and a bus 53 that connects different system components (including the memory 52 and the processing unit 51).

[0453] The bus 53 represents one or more of several types of bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. By way of example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0454] The electronic device typically includes a variety of computer system-readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, removable and non-removable media.

[0455] The memory 52 may include computer system-readable media in the form of volatile memory, such as random access memory (RAM) 521 and / or cache memory 522. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, a storage system 523 may be used for reading and writing on non-removable, non-volatile magnetic media ( Figure 5 not shown, commonly referred to as a "hard disk drive"). Although Figure 5 not shown in the figure, a disk drive for reading and writing on a removable non-volatile disk (such as a "floppy disk") and an optical disk drive for reading and writing on a removable non-volatile optical disk (such as a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 53 through one or more data media interfaces. The memory 52 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0456] A program / utility 524 having a set (at least one) of program modules 5241 may be stored, for example, in the memory 52. Such program modules 5241 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment. The program modules 5241 generally perform the functions and / or methods in the embodiments described in the present invention.

[0457] The electronic device can also communicate with one or more external devices (such as a keyboard, a pointing device, a display, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device, and / or communicate with any device that enables the electronic device to communicate with one or more other computing devices (such as a network card, a modem, etc.). Such communication can be carried out through the input / output (I / O) interface 54. Moreover, the electronic device can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 55. As Figure 5 shown, the network adapter 55 communicates with other modules of the electronic device through the bus 53. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in combination with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0458] The above embodiments are only illustrative of the principles and effects of the present invention, rather than limiting the present invention. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or changes completed by those with ordinary knowledge in the technical field without departing from the spirit and technical idea disclosed by the present invention should still be covered by the claims of the present invention.

Claims

1. A method for pulse condition dialectics, characterized in that, The method includes the following steps: Collect pulse condition data and obtain the characteristic parameters of the pulse condition data; Based on the pulse condition quantization standard, fit the pulse condition data and establish a pulse condition eigenvalue set database; Construct and train a pulse condition quantization mathematical model based on the pulse condition eigenvalue set database; Obtain the pulse waveform diagram of the person to be diagnosed; Input the pulse waveform diagram of the person to be diagnosed into the pulse condition quantization mathematical model to obtain the pulse condition diagnosis result.

2. The pulse condition dialectical method according to claim 1, characterized in that: Implement dynamic pressure application through a hybrid pressure sensor array to collect pulse condition data.

3. The pulse condition dialectical method according to claim 1, characterized in that: Divide the pulse condition data into 27 pulse condition types based on the pulse condition quantization standard, analyze each pulse condition type and obtain the corresponding eigenvalue set to establish a pulse condition eigenvalue set database.

4. The pulse condition dialectical method according to claim 1, characterized in that: Inputting the pulse waveform diagram of the person to be diagnosed into the pulse condition quantization mathematical model to obtain the pulse condition diagnosis result includes the following steps: Analyze the pulse waveform diagram of the person to be diagnosed through wavelet analysis method to obtain the characteristic parameters of the pulse condition data of the person to be diagnosed; Input the characteristic parameters of the pulse condition data of the person to be diagnosed into the pulse condition quantization mathematical model to obtain the corresponding pulse condition type; Combined with clinical information, analyze and judge the pulse condition type to generate a comprehensive decision of pulse evidence.

5. The pulse condition dialectical method according to claim 1, characterized in that, The characteristic parameters include: main wave height, tidal wave height, mid-descending notch height, dicrotic wave height, maximum peak value of the wave crest in the first low-pressure area, maximum peak value of the wave crest in the second low-pressure area, time value from the starting point of the pulse wave diagram to the main wave crest point, time value from the starting point of the pulse wave diagram to the mid-descending notch, time value from the mid-descending notch to the ending point of the pulse wave diagram, time value from the starting point to the ending point of the pulse wave diagram, first main wave width, second main wave width, mean pulse rate, pulse rate steady state, pulse rate variability, mean of the average pulse period difference of the pulse, stability coefficient of the average pulse period difference, initial pressure value of the pressure sensor probe, maximum downward pressure value of the pressure sensor probe, curvature of the main wave crest.

6. The pulse condition dialectical method according to claim 1, characterized in that: The pulse condition quantization mathematical model adopts a pulse condition judgment matrix algorithm.

7. The pulse condition dialectical method according to claim 1, characterized in that: After inputting the pulse waveform diagram of the person to be diagnosed into the pulse condition quantization mathematical model to obtain the pulse condition diagnosis result, it further includes generating a treatment plan based on the pulse condition diagnosis result.

8. A pulse condition dialectical system, characterized in that, The system includes a collection module, a fitting module, a quantization module, an acquisition module, and a diagnosis module; The collection module is used to collect pulse condition data and obtain the characteristic parameters of the pulse condition data; The fitting module is used to fit the pulse condition data and establish a pulse condition eigenvalue set database based on the pulse condition quantization standard; The quantization module is used to construct and train a pulse condition quantization mathematical model based on the pulse condition eigenvalue set database; The acquisition module is used to obtain the pulse waveform diagram of the person to be diagnosed; The diagnosis module is used to input the pulse waveform diagram of the person to be diagnosed into the pulse condition quantization mathematical model to obtain the pulse condition diagnosis result.

9. An electronic device, characterized in that, The electronic device includes: a processor and a memory; The memory is used to store a computer program; The processor is used to execute the computer program stored in the memory so that the electronic device executes the pulse condition diagnosis method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the electronic device, it implements the pulse condition diagnosis method according to any one of claims 1 to 7.

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