Pulse analysis method based on pulse diagnosis instrument and pulse diagnosis monitoring system

Abstract

The embodiment of the invention relates to the technical field of pulse diagnosis, and discloses a pulse analysis method based on a pulse diagnosis instrument, which comprises the following steps: processing a pressure change signal within a set time to obtain an original pulse waveform signal associated with a user; performing high-frequency denoising processing on the first pulse frequency signal according to a set noise threshold value; acquiring all wavelet coefficients associated with the high-frequency detail signal, traversing all the wavelet coefficients, determining that the absolute value of the wavelet coefficient at a certain point is greater than the absolute value of the wavelet coefficient adjacent to the wavelet coefficient, and taking the wavelet coefficient as a signal abrupt change point; generating a corresponding pulse image of the user based on the pulse condition data of the user within a set time range; and inputting the obtained pulse condition graph of the user into a pre-constructed standard pulse condition model to determine a pulse condition type result of the user, and outputting the pulse condition type result of the user. According to the scheme provided by the embodiment of the invention, the user can conveniently, quickly and accurately know the pulse condition and the physique state of the user, and the use convenience is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic equipment, and in particular to a pulse analysis method based on a pulse diagnosis instrument and a pulse diagnosis monitoring system. Background Art

[0002] At present, pulse diagnosis is one of the four diagnostic methods in traditional Chinese medicine and is a unique diagnostic method. It mainly uses the feeling of fingers to analyze the "position, number, shape, and potential" of the pulse to judge the functional state of the internal organs, thereby achieving the purpose of non-invasive diagnosis, which has a positive significance for the diagnosis and treatment of diseases. Although there are some pulse diagnosis instruments on the market to detect the user's pulse, their detection accuracy is not high and cannot provide better data basis. Therefore, designing a solution that can perform more accurate detection has become a technical problem that needs to be solved urgently by technicians in this field. Summary of the invention

[0003] In view of the above-mentioned defects, an embodiment of the present invention discloses a pulse analysis method based on a pulse diagnosis instrument, which can realize accurate detection of pulse type.

[0004] The first aspect of the embodiment of the present invention discloses a pulse analysis method based on a pulse diagnosis instrument, comprising:

[0005] Acquire a pressure change signal detected by a pressure detection component provided at the pulse diagnosis instrument within a set time, and process the pressure change signal within the set time to obtain an original pulse waveform signal associated with the user;

[0006] Decomposing the original pulse waveform signal according to the selected wavelet basis function and decomposition scale parameter to obtain a first pulse frequency signal and a second pulse frequency signal at different decomposition scales; wherein the first pulse frequency signal is used to characterize a high-frequency detail signal, the high-frequency detail signal includes high-frequency white noise and power frequency noise, and the second pulse frequency noise is used to characterize a low-frequency approximation signal;

[0007] According to the set noise threshold, the first pulse frequency signal is subjected to high-frequency denoising to remove high-frequency white noise and power frequency noise in the high-frequency detail signal; all wavelet coefficients associated with the high-frequency detail signal are acquired, all wavelet coefficients are traversed, and the absolute value of the wavelet coefficient at a certain point is determined to be greater than the absolute value of its adjacent wavelet coefficient, and the point is used as a signal mutation point;

[0008] Determine the pulse cycle characteristic value according to all signal mutation points, determine the user pulse data within a set time range according to the pulse cycle characteristic value, and generate a corresponding user pulse diagram based on the user pulse data within the set time range;

[0009] The obtained user pulse diagram is input into a pre-built standard pulse model to determine the user's pulse type result, and the user's pulse type result is output.

[0010] As an optional implementation mode, in the first aspect of the embodiment of the present invention, the user pulse data within the set time range is used to generate a corresponding user pulse image diagram, including:

[0011] Acquire user pulse data within a set time range, and construct a cubic polynomial equation for each user pulse data, wherein the cubic polynomial equation is the same at each data point and the first-order derivative and the second-order derivative between adjacent intervals are continuous;

[0012] The obtained cubic polynomial equation is calculated to obtain parameter values ​​of each coefficient in the cubic polynomial equation, and a pulse fitting curve is constructed on the interval of each data point according to the calculated parameter values ​​of each coefficient in the cubic polynomial equation;

[0013] According to the error values ​​between the user's pulse data and the pulse fitting curve, if all the error values ​​are within an error threshold, the pulse fitting curve is determined as the user's pulse diagram.

[0014] As an optional implementation manner, in the first aspect of the embodiment of the present invention, after acquiring all the wavelet coefficients associated with the high-frequency detail signal, the method further includes:

[0015] All wavelet coefficients are screened according to a preset extreme value screening threshold, and all wavelet coefficients are traversed for the screened wavelet coefficients to determine that the absolute value of the wavelet coefficient at a certain point is greater than the absolute value of its adjacent wavelet coefficient;

[0016] And / or, receiving detection attribute information input by a user, where the detection attribute information includes user gender, detection time and detection season.

[0017] As an optional implementation, in the first aspect of the embodiment of the present invention, before obtaining the pressure change signal within a set time detected by the pressure detection component set at the pulse diagnosis instrument, it also includes:

[0018] Receive an arm measurement signal input by a user, and if the arm measurement signal is a left hand signal, start the first pressure sensor group to perform pressure detection on the corresponding area of ​​the user's wrist; if the arm measurement signal is a right hand signal, start the second pressure sensor group to perform pressure detection on the corresponding area of ​​the user's wrist;

[0019] The method of obtaining a pressure change signal detected by a pressure detection component provided at the pulse diagnosis instrument within a set time, and processing the pressure change signal within the set time to obtain an original pulse waveform signal associated with the user, further includes:

[0020] When the inflatable component reaches the first set state, the pressure change signals detected by the corresponding three pressure detection areas in the pressure detection component set at the pulse diagnosis instrument within the set time are obtained. If the pressure change signals meet the set requirements, the pressure change signals detected by the corresponding pressure detection areas are processed respectively to obtain the original pulse waveform signals associated with the user; the number of the original pulse waveform signals is three; if the pressure change signals do not meet the set requirements, the inflatable component is controlled to work according to the set adjustment state to obtain the waveform signal; wherein the pressure value in the set adjustment state is greater than the pressure value in the first set state;

[0021] When the inflatable component reaches the second set state, the pressure change signals detected by the corresponding three pressure detection areas in the pressure detection component provided at the pulse diagnosis instrument within the set time are obtained, and the pressure change signals detected by the corresponding pressure detection areas are processed respectively to obtain the original pulse waveform signals associated with the user; the number of the original pulse waveform signals is three;

[0022] When the inflation component reaches the third set state, the pressure change signals detected within the set time by the corresponding three pressure detection areas in the pressure detection component provided at the pulse diagnosis instrument are obtained, and the pressure change signals detected by the corresponding pressure detection areas are processed respectively to obtain original pulse waveform signals associated with the user; the number of the original pulse waveform signals is three.

[0023] As an optional implementation mode, in the first aspect of the embodiment of the present invention, the standard pulse model is obtained by the following steps:

[0024] Acquire a sample training set, the sample training set comprising pulse sample images; and divide the sample training set into a training set, a validation set and a test set;

[0025] Performing a preprocessing operation on the pulse sample image;

[0026] Inputting the pulse sample image after the preprocessing operation into the standard pulse model with completed parameter configuration for training and parameter configuration adjustment until the corresponding standard pulse model meets the training requirements, and saving the current optimal model parameters; the standard pulse model is a model constructed based on a convolutional neural network;

[0027] Use the trained standard pulse model to predict the pulse test images in the test set to obtain the pulse type to which each pulse test image belongs;

[0028] Generate a category confusion matrix based on the prediction results and true annotations of the standard pulse model; the rows of the category confusion matrix represent true categories, and the columns represent predicted categories;

[0029] Calculating overall performance indicators of the standard pulse model according to the category confusion matrix, wherein the overall performance indicators include accuracy, precision, recall rate and F1 score;

[0030] And according to the analysis result of the type confusion matrix, the standard pulse model is adjusted and optimized;

[0031] The method of inputting the obtained user pulse diagram into a pre-built standard pulse model to determine the user's pulse type result comprises:

[0032] The obtained user pulse diagram is calculated with the pre-built standard pulse model by using the Euclidean distance formula to obtain the similarity value with each standard pulse, and the maximum value among the similarity values ​​with each standard pulse is determined. If the maximum value among the similarity values ​​of each standard pulse model is greater than the similarity threshold, the user's pulse type result is determined.

[0033] As an optional implementation, in the first aspect of the embodiment of the present invention, the pulse analysis method further includes:

[0034] Obtain pressure change signals detected by corresponding three pressure detection areas in the pressure detection assembly provided at the pulse diagnosis instrument within a set time, and determine corresponding pulse position data according to the pressure change signals detected by corresponding three pressure detection areas within a set time;

[0035] The user's pulse diagram is identified through a waveform recognition algorithm to determine the corresponding pulse beat cycle, the amplitude of the Cun, Guan, and Chi waveforms, pulse width data, the interval time between each beat, the strength of the pulse fluctuation, the smoothness of the pulse fluctuation, the tension of the pulse, and the uniformity of the waveform;

[0036] Determine the corresponding pulse type based on the obtained pulse cycle, the amplitude of the Cun, Guan, and Chi waveforms, the pulse width data, the interval time between each beat, the strength of the pulse fluctuation, the smoothness of the pulse fluctuation, the tension of the pulse, and the uniformity of the waveform;

[0037] Determine the yang index, moisture index, qi and blood index, and blood stasis index of the corresponding user according to the pulse type and pulse-index mapping association;

[0038] The obtained Yang Qi index, dampness index, Qi and blood index and blood stasis index are input into the Zang-Fu calculation formula to obtain the corresponding Zang-Fu scores; the Zang-Fu calculation formula is:

[0039] Organ score = ∑(pulse condition * similarity * (yang index + dampness index + qi and blood index + blood stasis index));

[0040] The meridian score is obtained based on the comprehensive data of pulse condition, viscera and constitution and the meridian calculation formula, wherein the meridian calculation formula is: meridian score = ∑ (pulse condition * similarity + constitution dialectics * weight + viscera dialectics * weight);

[0041] Outputting the pulse type, organ score and meridian score to the corresponding user terminal;

[0042] And / or, after determining the pulse cycle characteristic value according to all signal mutation points, determining the user pulse data within the set time range according to the pulse cycle characteristic value, and generating a corresponding user pulse diagram based on the user pulse data within the set time range, further comprising:

[0043] Extracting key pulse characteristic parameters from the collected pulse waveform data, the pulse characteristic parameters including the shape and position of the main wave, the dicrotic wave, the descending isthmus, as well as the pulse period, frequency, amplitude, peak-to-peak value, rise time, and fall time;

[0044] Performing preprocessing operations on the extracted feature parameters, wherein the preprocessing operations include data cleaning, normalization and dimensionality reduction operations;

[0045] The pulse characteristic parameters of the newly collected pulse waveform data are input as observation data into the Bayesian model, and the Bayesian model calculates the posterior probabilities of various pulse types based on the prior knowledge base and the observation data, and outputs the most likely pulse type as the recognition result based on the calculated posterior probabilities;

[0046] After the pulse type result of the user is determined, the method further comprises:

[0047] Combining the recognition results of the Bayesian model and the results of waveform similarity recognition, a comprehensive judgment is made. If the conclusions drawn by the two are consistent or similar, the final pulse type can be determined.

[0048] As an optional implementation, in the first aspect of the embodiment of the present invention, the pulse analysis method further includes:

[0049] Obtain the user's facial image at each time detection point captured by the camera;

[0050] Inputting the user's facial image at each time detection point into a pre-built facial analysis model for recognition to determine the user's emotion type at each time detection point; wherein the facial analysis model is constructed by a convolutional neural network;

[0051] The emotion type of the user at each time detection point is input into a waveform modification model constructed based on the relationship between emotion and pulse to optimize the pulse parameters so as to perform a correction operation on the obtained user pulse diagram.

[0052] A second aspect of an embodiment of the present invention discloses a pulse diagnosis monitoring system, comprising:

[0053] A housing component, wherein the housing component is provided with a receiving cavity, wherein the receiving cavity is used to receive the wrist to be measured,

[0054] A first inflatable component, the first inflatable component includes a first air pump and a first air bag, the first air bag is arranged on the top of the accommodating cavity, and the first air pump is used to inflate the first air bag;

[0055] a second inflatable component, the second inflatable component comprising a second air pump and a second air bag, the second air bag being arranged at the bottom of the accommodating cavity, and the second air pump being used to inflate the first air bag;

[0056] A pressure detection component, the detection component is arranged on the first airbag, and the detection component is used to perform pressure detection on a wrist to be measured placed in the accommodating cavity;

[0057] A control module, wherein the first air pump, the second air pump and the pressure detection component are all electrically connected to the control module, and the control module is used to control the first air pump and the second air pump to operate according to the received measurement control signal so as to expand the first airbag and the second airbag, thereby enabling the pressure detection component arranged at the first airbag to detect the pulse of the user's wrist.

[0058] As an optional implementation, in the second aspect of the embodiment of the present invention, the number of the first airbags is two, and the two first airbags are symmetrically arranged on the top of the accommodating cavity; the pressure detection component is a flexible pressure sensor, and both ends of the flexible pressure sensor are respectively connected to the two first airbags;

[0059] The flexible pressure sensor is provided with a first pressure sensor group and a second pressure sensor group, the first pressure sensor group includes a first pressure detection area, a second pressure detection area and a third pressure detection area arranged in sequence longitudinally, and the second pressure sensor group includes a fourth pressure detection area, a fifth pressure detection area and a sixth pressure detection area arranged in sequence longitudinally; and each pressure detection area includes a plurality of pressure detection points arranged in sequence transversely;

[0060] During the first pressure sensor detection process, pressure changes in corresponding positions of the user's wrist are detected through the first pressure detection area, the second pressure detection area and the third pressure detection area respectively. During the second pressure sensor detection process, pressure changes in corresponding positions of the user's wrist are detected through the fourth pressure detection area, the fifth pressure detection area and the sixth pressure detection area respectively.

[0061] As an optional implementation, in the second aspect of the embodiment of the present invention, the number of the first airbags is two, and the two first airbags are symmetrically arranged on the top of the accommodating cavity; the pressure detection component is a flexible pressure sensor, and both ends of the flexible pressure sensor are respectively connected to the two first airbags;

[0062] The flexible pressure sensor is provided with a first pressure sensor group and a second pressure sensor group, the first pressure sensor group includes a first pressure detection area, a second pressure detection area and a third pressure detection area arranged in sequence longitudinally, and the second pressure sensor group includes a fourth pressure detection area, a fifth pressure detection area and a sixth pressure detection area arranged in sequence longitudinally; and each pressure detection area includes a plurality of pressure detection points arranged in sequence transversely;

[0063] During the first pressure sensor detection process, pressure changes in corresponding positions of the user's wrist are detected through the first pressure detection area, the second pressure detection area and the third pressure detection area respectively. During the second pressure sensor detection process, pressure changes in corresponding positions of the user's wrist are detected through the fourth pressure detection area, the fifth pressure detection area and the sixth pressure detection area respectively.

[0064] As an optional implementation, in the second aspect of the embodiment of the present invention, the pulse diagnosis monitoring system further includes a background server, a communication module and a camera module, the camera module is used to transmit the monitored user facial expression and camera attributes to the background server through the communication module, wherein the camera attributes include camera number information and camera timestamp; the control module or the background server is used to execute the pulse analysis method based on the pulse diagnosis instrument as described in any one of the first aspects of the embodiment of the present invention;

[0065] The backend server is used to determine the corresponding pulse diagnosis instrument number according to the camera attributes and the camera-pulse diagnosis instrument mapping table, and the backend server is also used to recognize the user's facial expression to determine the user's current emotional state, and update the user's pulse data according to the current emotional state;

[0066] The control module is used to control the first air pump and the second air pump to work according to the first control parameter, the second control parameter and the third control parameter respectively according to the set three-level control mode;

[0067] And / or, the flexible pressure sensor includes 96 pressure monitoring points, each pressure detection area includes 16 pressure detection points arranged in sequence laterally, and the length of the first pressure detection area, the second pressure detection area, the third pressure detection area, the fourth pressure detection area, the fifth pressure detection area and the sixth pressure detection area is any value between 26 mm and 30 mm, and the width is any value between 6 mm and 8 mm.

[0068] The third aspect of an embodiment of the present invention discloses an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the pulse analysis method based on the pulse diagnosis instrument disclosed in the first aspect of the embodiment of the present invention.

[0069] A fourth aspect of an embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the pulse analysis method based on a pulse diagnosis instrument disclosed in the first aspect of an embodiment of the present invention.

[0070] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0071] In the embodiment of the present invention, the pressure detection component on the pulse diagnosis instrument can accurately capture the pressure change signal of the user's pulse within the set time, and then process and obtain the original pulse waveform signal. This ensures the accuracy and integrity of the pulse data and provides a reliable basis for subsequent analysis. The user's pulse diagram is input into the pre-built standard pulse model, and the user's pulse type result can be automatically determined, realizing the intelligence and automation of pulse analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0073] Figure 1 It is a flow chart of a pulse analysis method based on a pulse diagnosis instrument disclosed in an embodiment of the present invention;

[0074] Figure 2 It is a schematic flow diagram of pulse curve fitting disclosed in an embodiment of the present invention;

[0075] Figure 3 It is a schematic diagram of a floating pulse detected by an embodiment of the present invention;

[0076] Figure 4 Schematic diagram of the detected central pulse condition disclosed in the embodiment of the present invention

[0077] Figure 5 It is a schematic diagram of the detected deep pulse condition disclosed in the embodiment of the present invention;

[0078] Figure 6 Schematic diagram of a pulse condition of a normal pulse width disclosed in an embodiment of the present invention;

[0079] Figure 7 is a pulse diagram of a Hong pulse disclosed in an embodiment of the present invention;

[0080] Figure 8 Schematic diagram of the pulse condition of the fine pulse disclosed in the embodiment of the present invention;

[0081] Fig. 9 The structure diagram of the pulse diagnosis instrument disclosed in the embodiment of the present invention is

[0082] Fig.10 It is a schematic diagram of the exploded structure of the pulse diagnosis instrument disclosed in the embodiment of the present invention;

[0083] Fig.11 is a cross-sectional view of a pulse diagnosis instrument disclosed in an embodiment of the present invention;

[0084] Fig.12 is a schematic diagram of the structure of a pressure detection assembly disclosed in an embodiment of the present invention;

[0085] Fig.13 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention.

[0086] Figure numerals: 1. Shell assembly; 11. Upper shell; 12. Main shell; 13. Lower shell; 2. Accommodating chamber; 3. First air pump; 4. Second air pump; 5. First airbag; 6. Second airbag; 7. Pressure detection assembly; 71. First pressure detection area; 72. Second pressure detection area; 73. Third pressure detection area; 74. Fourth pressure detection area; 75. Fifth pressure detection area; 76. Sixth pressure detection area; 8. Control module. DETAILED DESCRIPTION

[0087] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0088] It should be noted that the terms "first", "second", "third", "fourth", etc. in the specification and claims of the present invention are used to distinguish different objects rather than to describe a specific order. The terms "including" and "having" in the embodiments of the present invention and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0089] Although there are some pulse diagnosis instruments on the market to detect the pulse of users, their detection accuracy is not high and they cannot provide better data basis. Based on this, the embodiment of the present invention discloses a pulse analysis method, system, electronic device and storage medium based on the pulse diagnosis instrument, which can accurately capture the pressure change signal of the user's pulse within a set time through the pressure detection component on the pulse diagnosis instrument, and then process and obtain the original pulse waveform signal. This ensures the accuracy and integrity of the pulse data and provides a reliable basis for subsequent analysis. The user's pulse diagram is input into a pre-constructed standard pulse model, and the user's pulse type result can be automatically judged, realizing the intelligence and automation of pulse analysis.

[0090] Embodiment 1

[0091] See also Figure 1 , Figure 1 It is a flow chart of the pulse analysis method based on the pulse diagnosis instrument disclosed in the embodiment of the present invention. Among them, the execution subject of the method described in the embodiment of the present invention is an execution subject composed of software and / or hardware, and the execution subject can receive relevant information by wired or / and wireless means, and can send certain instructions. Of course, it can also have certain processing functions and storage functions. The execution subject can control multiple devices, such as a remote physical server or cloud server and related software, or it can be a local host or server and related software that performs related operations on a device placed somewhere. In some scenarios, multiple storage devices can also be controlled, and the storage devices can be placed in the same place or different places as the devices. For example Figure 1 As shown, the pulse analysis method based on the pulse diagnosis instrument includes the following steps:

[0092] S101: obtaining a pressure change signal detected by a pressure detection component provided at a pulse diagnosis instrument within a set time, and processing the pressure change signal within the set time to obtain an original pulse waveform signal associated with the user;

[0093] S102: performing waveform decomposition on the original pulse waveform signal according to the selected wavelet basis function and decomposition scale parameter to obtain a first pulse frequency signal and a second pulse frequency signal at different decomposition scales; wherein the first pulse frequency signal is used to characterize a high-frequency detail signal, the high-frequency detail signal includes high-frequency white noise and power frequency noise, and the second pulse frequency noise is used to characterize a low-frequency approximation signal;

[0094] S103: performing high-frequency denoising processing on the first pulse frequency signal according to a set noise threshold to remove high-frequency white noise and power frequency noise in the high-frequency detail signal; obtaining all wavelet coefficients associated with the high-frequency detail signal, traversing all wavelet coefficients and determining that the absolute value of the wavelet coefficient at a certain point is greater than the absolute value of its adjacent wavelet coefficient, and taking it as a signal mutation point;

[0095] S104: determining a pulse cycle characteristic value according to all signal mutation points, determining user pulse data within a set time range according to the pulse cycle characteristic value, and generating a corresponding user pulse diagram based on the user pulse data within the set time range;

[0096] S105: Input the obtained user pulse diagram into a pre-constructed standard pulse model to determine the user's pulse type result, and output the user's pulse type result.

[0097] The method of the embodiment of the present invention first obtains the pressure change signal within a set time through the pressure detection component of the pulse diagnosis instrument, and processes it to obtain the original pulse waveform signal. This step ensures the accurate capture of the pulse signal and provides a reliable basis for subsequent analysis.

[0098] The original pulse waveform signal is decomposed using wavelet basis functions and decomposition scale parameters to obtain the first pulse frequency signal (high-frequency detail signal) and the second pulse frequency signal (low-frequency approximation signal) at different decomposition scales. This method can effectively separate different components in the pulse signal.

[0099] Next, the high-frequency detail signal is denoised according to the set noise threshold to remove high-frequency white noise and power frequency noise. This step improves the signal-to-noise ratio of the pulse signal, making subsequent analysis more accurate. On the basis of preprocessing, high-frequency noise and low-frequency noise are further removed. High-frequency noise may come from the noise of the equipment itself or the interference of the external environment, while low-frequency noise may come from the interference of physiological activities such as breathing and heartbeat. The purpose of denoising is to improve the signal-to-noise ratio of the signal, so that subsequent feature extraction and classification recognition are more accurate. In the denoised pulse signal, the pulse cycle characteristic values ​​are extracted using algorithms such as the modulus maximum principle. These characteristic values ​​include the main wave peak amplitude, the main wave gorge amplitude, the amplitude of the dicrotic pre-wave, etc., which can reflect different aspects of the pulse signal. In actual signal processing, high-frequency detail signals and low-frequency detail signals are complementary. High-frequency detail signals are mainly used to detect the mutation points or detail information of the signal, while low-frequency detail signals are used to reflect the main trend or smooth part of the signal. By combining these two signals, we can have a more comprehensive understanding of the characteristics and properties of the signal. The low-frequency detail signal contains the main low-frequency components of the pulse signal, which reflect the overall trend of the pulse signal, such as the periodic changes of the pulse, the average heart rate, etc.

[0100] The pulse cycle characteristic value is identified by determining the signal mutation point. This method can accurately capture the periodic changes in the pulse signal, thereby accurately reflecting the pulse characteristics of the user. The user pulse data within the set time range is determined based on the pulse cycle characteristic value, and the corresponding user pulse diagram is generated. This provides intuitive pulse information for the user, which is convenient for subsequent health analysis. The obtained user pulse diagram is input into the pre-built standard pulse model to determine the user's pulse type result. This step realizes the intelligence and automation of pulse diagnosis, and improves the accuracy and efficiency of diagnosis. Output the user's pulse type result. The pulse analysis method based on the pulse diagnosis instrument realizes accurate, comprehensive and intelligent analysis of the user's pulse by accurately capturing the pulse signal, effectively decomposing and denoising, accurately identifying the pulse characteristics, and intelligent pulse diagnosis. This not only improves the accuracy and efficiency of traditional Chinese medicine diagnosis, but also provides strong support for the user's health management. When the waveform expression and the autocorrelation threshold of the common 28 pulses reach above 0.6, the pulse expression is considered to be valid.

[0101] More preferably, Figure 2 As shown, the user pulse data within the set time range is used to generate a corresponding user pulse image diagram, including:

[0102] S1041: acquiring user pulse data within a set time range, and constructing a cubic polynomial equation for each user pulse data, wherein the cubic polynomial equation is the same at each data point and the first-order derivative and the second-order derivative between adjacent intervals are continuous;

[0103] S1042: Calculate the obtained cubic polynomial equation to obtain parameter values ​​of each coefficient in the cubic polynomial equation, and construct a pulse fitting curve in the interval of each data point according to the calculated parameter values ​​of each coefficient in the cubic polynomial equation;

[0104] S1043: According to the error values ​​between the user's pulse data and the pulse fitting curve, if all the error values ​​are within an error threshold, the pulse fitting curve is determined as the user's pulse diagram.

[0105] The embodiment of the present invention can ensure that the equation is the same at each data point by constructing a cubic polynomial equation for each user's pulse data, and the first-order derivative and the second-order derivative between adjacent intervals are all continuous. This continuity ensures the smooth transition of the pulse data and avoids the analysis error caused by data mutation or jump. By calculating the parameter values ​​of each coefficient in the cubic polynomial equation and constructing a pulse fitting curve in the interval of each data point, the accurate fitting of the pulse waveform can be achieved. This fitting method not only takes into account the accuracy of the data points, but also takes into account the changing trend between the data, thereby improving the precision and reliability of the fitting.

[0106] By comparing the error value between the user's pulse data and the pulse fitting curve, the accuracy of the fitting can be evaluated. When all error values ​​are within the error threshold, the pulse fitting curve is determined as the user's pulse diagram, which ensures the accuracy and reliability of the pulse diagram. This verification step helps to reduce the error caused by improper fitting and improves the accuracy of pulse analysis. Adopting a cubic polynomial equation for fitting and verification can automatically generate a user's pulse diagram without manual intervention. This not only improves the efficiency of pulse analysis, but also makes the pulse analysis process more intelligent and automatic.

[0107] More preferably, after acquiring all the wavelet coefficients associated with the high-frequency detail signal, the method further includes:

[0108] All wavelet coefficients are screened according to a preset extreme value screening threshold, and all wavelet coefficients are traversed for the screened wavelet coefficients to determine that the absolute value of the wavelet coefficient at a certain point is greater than the absolute value of its adjacent wavelet coefficient;

[0109] And / or, receiving detection attribute information input by a user, where the detection attribute information includes user gender, detection time and detection season.

[0110] After acquiring all the wavelet coefficients associated with the high-frequency detail signal, the scheme of the embodiment of the present invention can remove those extreme points that obviously do not conform to the characteristics of the pulse signal by screening the wavelet coefficients through a preset extreme value screening threshold. This can reduce the impact of noise and outliers on the identification of signal mutation points, thereby improving the accuracy of identification. The screened wavelet coefficients are traversed to determine the absolute value of the wavelet coefficient at a certain point that is greater than the absolute value of its adjacent wavelet coefficients as the signal mutation point. This step further refines the identification of mutation points and ensures that the mutation points can accurately reflect the periodic changes of the pulse signal.

[0111] The embodiment of the present invention can make pulse analysis more personalized by receiving detection attribute information (such as user gender, detection time and detection season) input by the user. There may be certain differences in pulses under different genders, different times and seasons. By considering these attribute information, pulse data can be interpreted more accurately, improving the accuracy and pertinence of analysis.

[0112] In the embodiments of the present invention, the special cases are mainly described in the following aspects: Gender differences. Women have slippery pulse, moist pulse, weak pulse, etc., which are more common in women than in men, so they are mostly normal. Seasonal differences. Adjustment is performed according to the pulse characteristics of the four seasons. String pulse is common in spring, flood pulse is common in summer, floating pulse is common in autumn, and sinking pulse is common in winter. Compared with other seasons, the above pulse conditions appear in the corresponding seasons, and each indicator tends to be normal.

[0113] More preferably, before obtaining the pressure change signal detected by the pressure detection component provided at the pulse diagnosis instrument within a set time, the method further includes:

[0114] Receive an arm measurement signal input by a user, and if the arm measurement signal is a left hand signal, start the first pressure sensor group to perform pressure detection on the corresponding area of ​​the user's wrist; if the arm measurement signal is a right hand signal, start the second pressure sensor group to perform pressure detection on the corresponding area of ​​the user's wrist;

[0115] The method of obtaining a pressure change signal detected by a pressure detection component provided at the pulse diagnosis instrument within a set time, and processing the pressure change signal within the set time to obtain an original pulse waveform signal associated with the user, further includes:

[0116] When the inflatable component reaches the first set state, the pressure change signals detected by the corresponding three pressure detection areas in the pressure detection component set at the pulse diagnosis instrument within the set time are obtained. If the pressure change signals meet the set requirements, the pressure change signals detected by the corresponding pressure detection areas are processed respectively to obtain the original pulse waveform signals associated with the user; the number of the original pulse waveform signals is three; if the pressure change signals do not meet the set requirements, the inflatable component is controlled to work according to the set adjustment state to obtain the waveform signal; wherein the pressure value in the set adjustment state is greater than the pressure value in the first set state;

[0117] When the inflatable component reaches the second set state, the pressure change signals detected by the corresponding three pressure detection areas in the pressure detection component provided at the pulse diagnosis instrument within the set time are obtained, and the pressure change signals detected by the corresponding pressure detection areas are processed respectively to obtain the original pulse waveform signals associated with the user; the number of the original pulse waveform signals is three;

[0118] When the inflation component reaches the third set state, the pressure change signals detected within the set time by the corresponding three pressure detection areas in the pressure detection component provided at the pulse diagnosis instrument are obtained, and the pressure change signals detected by the corresponding pressure detection areas are processed respectively to obtain original pulse waveform signals associated with the user; the number of the original pulse waveform signals is three.

[0119] Specifically, when the pulse diagnosis instrument is started, it will inform the instrument whether it is left-hand or right-hand detection; 48 monitoring points are taken on one side, and the changes in pressure values ​​are observed at the same time, among which: the position of the Cun pulse is the left hand [1-16], and the right hand [81-96]; the position of the Guan pulse is the left hand [17-32], and the right hand [65-80]; the position of the Chi pulse is the left hand [33-48], and the right hand [49-64]; the upper air bag is pressurized to simulate the floating, middle and sinking of traditional Chinese medicine. Through multiple experimental analyses, it is concluded that the optimal pressure is 12000Pa for floating, 15000Pa for middle, and 18000Pa for sinking. For pulses that are relatively deep or not significant, the optimal pressure is 15000Pa for floating, 18000Pa for middle, and 20000Pa for sinking.

[0120] The pulses with the largest correlation threshold are taken from Cun, Guan and Chi as observable pulse diagrams. Under the three pressure levels of floating, medium and sinking, the three selected points of Cun, Guan and Chi are sampled for 15 seconds at the same time to obtain three segments of raw data, and the pulse diagram is obtained by repeatedly using the wavelet transform method.

[0121] When the inflatable component reaches different set states (first set state, second set state, third set state), the system respectively obtains the pressure change signals detected by the corresponding three pressure detection areas in the pressure detection component set at the pulse diagnosis instrument. This staged and pressure-divided detection method can more comprehensively capture the subtle changes in the user's pulse and improve the accuracy of pulse analysis.

[0122] At the same time, the system also determines whether to acquire the waveform signal or adjust the working state of the inflation component according to whether the pressure change signal meets the set requirements. This intelligent control strategy ensures the stability and reliability of the measurement process.

[0123] In each inflation state, the system obtains the pressure change signals detected by the three pressure detection areas respectively, and processes them to obtain three original pulse waveform signals. This not only increases the amount of original data, but also provides richer information for subsequent pulse analysis and diagnosis. The three areas here correspond to the user's Cun, Guan, and Chi respectively. By dividing the area, it is convenient for the user to conduct subsequent in-depth analysis and determination.

[0124] More preferably, the standard pulse model is obtained by the following steps:

[0125] Acquire a sample training set, the sample training set comprising pulse sample images; and divide the sample training set into a training set, a validation set and a test set;

[0126] Performing a preprocessing operation on the pulse sample image;

[0127] Inputting the pulse sample image after the preprocessing operation into the standard pulse model with completed parameter configuration for training and parameter configuration adjustment until the corresponding standard pulse model meets the training requirements, and saving the current optimal model parameters; the standard pulse model is a model constructed based on a convolutional neural network;

[0128] Use the trained standard pulse model to predict the pulse test images in the test set to obtain the pulse type to which each pulse test image belongs;

[0129] Generate a category confusion matrix based on the prediction results and true annotations of the standard pulse model; the rows of the category confusion matrix represent true categories, and the columns represent predicted categories;

[0130] Calculating overall performance indicators of the standard pulse model according to the category confusion matrix, wherein the overall performance indicators include accuracy, precision, recall rate and F1 score;

[0131] And according to the analysis result of the type confusion matrix, the standard pulse model is adjusted and optimized;

[0132] The method of inputting the obtained user pulse diagram into a pre-built standard pulse model to determine the user's pulse type result comprises:

[0133] The obtained user pulse diagram is calculated with the pre-built standard pulse model by using the Euclidean distance formula to obtain the similarity value with each standard pulse, and the maximum value among the similarity values ​​with each standard pulse is determined. If the maximum value among the similarity values ​​of each standard pulse model is greater than the similarity threshold, the user's pulse type result is determined.

[0134] The embodiment of the present invention can ensure the data diversity and independence in the model training process by obtaining a sample training set containing pulse sample images and performing a division operation to obtain a training set, a verification set and a test set. This helps to improve the accuracy and generalization ability of the model. Pre-processing operations such as denoising and enhancement are performed on the pulse sample images to further improve the image quality, thereby improving the effect of model training. The standard pulse model based on convolutional neural network (CNN) is adopted for training, and the training set and the verification set are used to adjust the parameter configuration and optimize the model, which can ensure that the model can learn the deep features in the pulse image and improve the classification accuracy of the model.

[0135] By using the pulse test images in the test set to predict the trained standard pulse model, the pulse type of each pulse test image can be obtained. By comparing the prediction results with the true annotations, a category confusion matrix can be generated, and then the overall performance indicators of the model (accuracy, precision, recall rate and F1 score) can be calculated. This helps to comprehensively evaluate the performance of the model and provide direction for subsequent optimization.

[0136] By using the Euclidean distance formula to calculate the similarity value between the user's pulse image and the pre-built standard pulse model, and determining the maximum value in the similarity value with each standard pulse, the user's pulse type can be determined more accurately. This method not only takes into account the overall characteristics of the pulse image, but also takes into account the similarity between the pulse images, thereby improving the accuracy of matching. A similarity threshold is set, and when the maximum value in the similarity value with each standard pulse model is greater than the similarity threshold, the user's pulse type result is determined. This strategy can further reduce the possibility of misjudgment and improve the accuracy of pulse analysis. By optimizing the acquisition process of the standard pulse model and the matching process of the user's pulse image and the standard pulse model, pulse analysis can be made more efficient and practical. This not only helps to improve the accuracy and efficiency of Chinese medicine diagnosis, but also provides more convenient and personalized support for the user's health management.

[0137] In the embodiment of the present invention, by analyzing the similarity of waveforms, pulse conditions such as flat pulse, stringy pulse, slippery pulse, astringent pulse, surging pulse, thin pulse, weak pulse, strong pulse, tight pulse, and moist pulse can be effectively distinguished.

[0138] When constructing a specific standard model, pulse data is sampled through a traditional Chinese medicine pulse training instrument. The detection data is manually labeled, classified and sorted to provide training, learning and materials for the AI ​​model. The system provides a data labeling background to manually label the recorded standard pulse data to obtain manual labeling results.

[0139] More preferably, the pulse analysis method further comprises:

[0140] Obtain pressure change signals detected by corresponding three pressure detection areas in the pressure detection assembly provided at the pulse diagnosis instrument within a set time, and determine corresponding pulse position data according to the pressure change signals detected by corresponding three pressure detection areas within a set time;

[0141] The user's pulse diagram is identified through a waveform recognition algorithm to determine the corresponding pulse beat cycle, the amplitude of the Cun, Guan, and Chi waveforms, pulse width data, the interval time between each beat, the strength of the pulse fluctuation, the smoothness of the pulse fluctuation, the tension of the pulse, and the uniformity of the waveform;

[0142] Determine the corresponding pulse type based on the obtained pulse cycle, the amplitude of the Cun, Guan, and Chi waveforms, the pulse width data, the interval time between each beat, the strength of the pulse fluctuation, the smoothness of the pulse fluctuation, the tension of the pulse, and the uniformity of the waveform;

[0143] Determine the yang index, moisture index, qi and blood index, and blood stasis index of the corresponding user according to the pulse type and pulse-index mapping association;

[0144] The obtained Yang Qi index, moisture index, Qi and blood index and blood stasis index are input into the Zang-Fu calculation formula to obtain the corresponding Zang-Fu scores; the Zang-Fu calculation formula is:

[0145] Organ score = ∑(pulse condition * similarity * (yang index + dampness index + qi and blood index + blood stasis index));

[0146] The meridian score is obtained based on the comprehensive data of pulse condition, viscera and constitution and the meridian calculation formula, wherein the meridian calculation formula is: meridian score = ∑ (pulse condition * similarity + constitution dialectics * weight + viscera dialectics * weight);

[0147] Outputting the pulse type, organ score and meridian score to the corresponding user terminal;

[0148] In the embodiment of the present invention, a waveform recognition algorithm is used to recognize the pulse image, and detailed data such as pulse beat period, amplitude, pulse width, pulse interval time, pulse strength, smoothness, tension and waveform uniformity can be extracted. These fine pulse characteristics provide rich information for subsequent pulse type judgment and physical analysis.

[0149] Specifically, pulse rate refers to the pulse cycle, which is aimed at pulses with fixed cycles. The frequency of the cycle can be inferred from the pulse frequency, and rapid pulse, flat pulse, slow pulse, slow pulse, and fast pulse can be distinguished. Rapid pulse: 90-120 beats per minute, flat pulse: 70-90 beats per minute. Slow pulse: less than 60 beats per minute, slow pulse: 60-70 beats per minute, fast pulse: more than 120 beats per minute.

[0150] Pulse length refers to the amplitude of the waveform of the three fingers corresponding to Cun, Guan, and Chi to distinguish long pulses or short pulses. Long pulse: The pulse amplitude of Cun and Chi is close to that of Guan pulse (10% is used as the judgment threshold), which is judged as a long pulse. Short pulse: The pulse amplitude of Cun or Chi is low, or cannot be detected when floating, or the amplitude is less than 40% of Guan pulse, which is judged as a short pulse.

[0151] Pulse width mainly refers to the amplitude of the pulse wave, which is generally used to distinguish between strong pulse, weak pulse and other related pulse conditions. Figures 6 to 8 As shown, in normal situation: the pulse width corresponds to the pulse wave amplitude between 150-350, in strong pulse: the pulse width corresponds to the pulse wave amplitude greater than 350, and in thin pulse: the pulse width corresponds to the pulse wave amplitude less than 150.

[0152] According to the uniformity of the waveform and the interval time between each beat, the pulse can be divided into knotted pulse, accelerated pulse and alternate pulse. Knotted pulse: beats 60-70 times per minute, with irregular rhythm, and the length of time without beat is not consistent. Accelerated pulse: beats 90-120 times per minute, with irregular rhythm, and the length of time without beat is not consistent. Alternative pulse: fluctuates 60-70 times per minute, with irregular rhythm, but the rhythm of the stops is regular.

[0153] The 28 pulse conditions of traditional Chinese medicine are the first comprehensive manifestation of all characteristics. A single characteristic cannot effectively dialectically identify the pulse characteristics. Therefore, it is necessary to combine multiple parameters for specific treatment.

[0154] In the embodiment of the present invention, according to the extracted pulse characteristics, the corresponding pulse type can be determined, and the yang index, moisture index, qi and blood index and blood stasis index are determined by pulse-index mapping association. This association method not only takes into account the direct characteristics of the pulse, but also combines the concepts of constitution and qi and blood in Chinese medicine theory, so that the analysis result is more comprehensive and accurate. The calculation of the viscera score combines the pulse characteristics, similarity and the indexes such as yang, moisture, qi and blood and blood stasis. By comprehensively considering these factors, a more scientific viscera health status assessment can be obtained. The calculation of the meridian score further considers the factors such as pulse, constitution dialectics and viscera dialectics, and assigns different weights to reflect their importance in the meridian assessment. This comprehensive assessment method can more comprehensively reflect the health status of the user.

[0155] When carrying out the specific implementation, the pulse data reported by the pulse diagnosis instrument can be used to observe the three pulse stages of floating, middle and sinking. Figures 3 to 5 As shown, according to the floating and sinking of the pulse, the position characteristics of the floating pulse, sinking pulse, wiry pulse, weak pulse, surging pulse, firm pulse, moist pulse, leathery pulse, weak pulse, scattered pulse and hidden pulse can be distinguished. Figures 3 to 5 The pulse condition is recorded in the three categories of floating, middle and sinking. When it is floating, the pulse condition is obvious, and when it is sinking, the pulse condition is weak, thus it can be determined as a floating pulse.

[0156] More preferably, after determining the pulse cycle characteristic value according to all signal mutation points, determining the user pulse data within a set time range according to the pulse cycle characteristic value, and generating a corresponding user pulse diagram based on the user pulse data within the set time range, the method further comprises:

[0157] Extracting key pulse characteristic parameters from the collected pulse waveform data, the pulse characteristic parameters including the shape and position of the main wave, the dicrotic wave, the descending isthmus, as well as the pulse period, frequency, amplitude, peak-to-peak value, rise time, and fall time;

[0158] Performing preprocessing operations on the extracted feature parameters, wherein the preprocessing operations include data cleaning, normalization and dimensionality reduction operations;

[0159] The pulse characteristic parameters of the newly collected pulse waveform data are input as observation data into the Bayesian model, and the Bayesian model calculates the posterior probabilities of various pulse types based on the prior knowledge base and the observation data, and outputs the most likely pulse type as the recognition result based on the calculated posterior probabilities;

[0160] After the pulse type result of the user is determined, the method further comprises:

[0161] Combining the recognition results of the Bayesian model and the results of waveform similarity recognition, a comprehensive judgment is made. If the conclusions drawn by the two are consistent or similar, the final pulse type can be determined.

[0162] In the embodiment of the present invention, it is necessary to construct a priori knowledge base of pulse type characteristic parameters based on TCM theory and clinical practice. This knowledge base contains the characteristic parameter ranges and distributions of various TCM pulse types (such as flat pulse, stringy pulse, slippery pulse, etc.). The characteristic parameters of the newly collected pulse waveform data are input into the Bayesian model as observation data. The Bayesian model calculates the posterior probabilities of various pulse types based on the priori knowledge base and the observation data. In this process, the model will consider the matching degree between the observation data and the priori knowledge, and update the probability distribution of various pulse types accordingly.

[0163] The embodiment of the present invention extracts key pulse characteristic parameters from the collected pulse waveform data, such as the shape and position of the main wave, the dicrotic wave, and the descending isthmus, as well as the pulse period, frequency, amplitude, peak-to-peak value, rise time, fall time, etc. These characteristic parameters can fully reflect the subtle changes in the pulse waveform and provide accurate basic data for subsequent pulse type identification.

[0164] The extracted feature parameters are preprocessed, including data cleaning, normalization, and dimensionality reduction. Data cleaning can remove noise and outliers and improve data accuracy; normalization can convert data to the same scale for subsequent processing; dimensionality reduction can reduce redundant information and improve computational efficiency. These preprocessing operations can significantly improve the recognition performance and stability of the Bayesian model.

[0165] The pulse characteristic parameters of the newly collected pulse waveform data are input into the Bayesian model as observation data. The Bayesian model calculates the posterior probability of various pulse types based on the prior knowledge base and observation data, and outputs the most likely pulse type as the recognition result. This method combines prior knowledge and observation data to achieve efficient and accurate pulse type recognition.

[0166] Combine the recognition results of the Bayesian model and the results of waveform similarity recognition to make a comprehensive judgment. If the conclusions of the two are consistent or similar, the final pulse type can be determined. This comprehensive judgment method can make full use of the advantages of different recognition methods and improve the reliability and accuracy of the recognition results.

[0167] More preferably, the pulse analysis method further comprises:

[0168] Obtain the user's facial image at each time detection point captured by the camera;

[0169] Inputting the user's facial image at each time detection point into a pre-built facial analysis model for recognition to determine the user's emotion type at each time detection point; wherein the facial analysis model is constructed by a convolutional neural network;

[0170] The emotion type of the user at each time detection point is input into a waveform modification model constructed based on the relationship between emotion and pulse to optimize the pulse parameters so as to perform a correction operation on the obtained user pulse diagram.

[0171] Since human emotions do have an impact on the measurement of pulse waves, in the specific implementation, the user's emotional state can be grasped by setting a camera; then the pulse wave waveform can be corrected in combination with the user's emotional state; the embodiment of the present invention uses a camera to capture the user's facial image at each time detection point, and uses a pre-built facial analysis model for emotion recognition. This step incorporates emotional factors into the pulse analysis process. Emotion is one of the important factors that affect the physiological state of the human body, and its changes are often accompanied by pulse fluctuations. Therefore, taking emotional factors into account can make pulse analysis more comprehensive and accurate. The facial analysis model is constructed by a convolutional neural network, which has a powerful ability to process image data and can accurately identify the user's emotional type. By automatically learning the features in the image, the convolutional neural network can capture subtle changes in facial expressions, thereby achieving accurate recognition of emotions.

[0172] By inputting the user's emotion type at each time detection point into the waveform modification model constructed based on the relationship between emotion and pulse, the pulse parameters can be optimized, and then the obtained user pulse diagram can be corrected. This step takes into account the impact of emotion on the pulse, and adjusts the pulse parameters to reflect the emotional changes, thereby improving the accuracy and reliability of the pulse diagram.

[0173] By combining emotion recognition and pulse analysis, users can be provided with a more personalized health assessment. Under different emotional states, the human body's physiological reactions will also be different. Therefore, when assessing the user's health status, considering emotional factors can make the assessment results closer to the user's actual situation and provide users with more targeted health advice.

[0174] In the embodiment of the present invention, a machine learning algorithm is used to make corrections. For example, a regression model can be used to establish a relationship model between emotions and pulse, and the model can be used to correct the pulse measurement data. In addition, a classification algorithm can be used to identify the type of emotion, and different corrections can be made to the pulse measurement data according to different types of emotions. A large number of data sets containing emotion labels and pulse measurement data are collected; a model is trained using a machine learning algorithm to establish a relationship between emotions and pulse; the trained model is applied to new pulse measurement data for correction calculation.

[0175] In the embodiment of the present invention, a pressure sensor array can also be combined to realize automatic pulse finding; the pressure sensor array can be arranged in the radial artery area of ​​the wrist, which is a common pulse taking position in traditional Chinese medicine pulse diagnosis.

[0176] Each sensor in the array can independently detect the pressure changes at its location, thereby achieving comprehensive capture of the pulse signal. When the user places the wrist on the sensor array, the sensors in the array will begin to collect pulse signals. The collected signals will undergo pre-processing operations such as filtering and amplification to improve the signal-to-noise ratio and accuracy of the signal. Based on the collected pulse signal, the system can use an automatic pulse-finding algorithm to determine the exact location of the pulse. The algorithm can identify the pulse signal and determine its location by analyzing the waveform, frequency, amplitude and other characteristics of the signal. The system can adjust the layout or acquisition parameters of the sensor array in real time according to the results of the automatic pulse-finding algorithm to ensure accurate capture of the pulse signal. For example, if the algorithm determines that the pulse position is offset, the system can automatically adjust the position of the sensor array or reallocate the acquisition task.

[0177] In the embodiment of the present invention, advanced facial emotion recognition technology, such as deep learning models (such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), etc.), is used to recognize the user's facial emotions in real time. When training the model, a facial image dataset containing multiple emotion labels is used to ensure that the model can accurately recognize various emotions. The recognition results are post-processed, such as smoothing filtering, threshold processing, etc., to improve the stability and accuracy of recognition.

[0178] In addition to the above features, other facial features can also be identified to assist in subsequent processing; the other facial features here refer to facial features that are helpful for TCM judgment, such as facial color: Yellow: may reflect poor spleen and stomach, digestive system function. White: may indicate problems with the lungs and respiratory system. Red: may be related to abnormalities in the cardiovascular system. Cyan: may indicate liver and gallbladder, immune system dysfunction; Black: may reflect poor kidney and bladder system condition.

[0179] Facial luster and appearance: If the complexion is bright and moist, it means that the vital energy of the internal organs is not weakened; if the complexion is dull and dark, it means that the vital energy of the internal organs is greatly weakened. Changes in facial appearance, such as swelling and weight loss, may also reflect internal organ diseases.

[0180] Facial reflexology: According to traditional Chinese medicine, different facial areas correspond to internal organs. For example, the forehead is related to the brain, throat, lungs and heart; the philtrum is related to the uterus and external bladder; the nose reflects the spleen and stomach; the zygomatic area is related to the large intestine and kidneys, etc. By observing the changes in the color and shape of these reflexology areas, we can further understand the health of the internal organs. The above characteristics can provide more comprehensive data.

[0181] In the embodiment of the present invention, the pressure detection component on the pulse diagnosis instrument can accurately capture the pressure change signal of the user's pulse within the set time, and then process and obtain the original pulse waveform signal. This ensures the accuracy and integrity of the pulse data and provides a reliable basis for subsequent analysis. The user's pulse diagram is input into the pre-built standard pulse model, and the user's pulse type result can be automatically determined, realizing the intelligence and automation of pulse analysis.

[0182] Embodiment 2

[0183] like Figures 9 to 12 As shown, the embodiment of the present invention discloses a pulse diagnosis monitoring system, comprising:

[0184] The housing assembly 1 is provided with a receiving cavity 2, and the receiving cavity 2 is used to receive the wrist to be measured; specifically, the housing assembly 1 comprises an upper housing 11, a main housing 12 and a lower housing 13 which are sequentially stacked and installed; the main housing 12 is provided with a receiving cavity 2;

[0185] a first inflatable component, the first inflatable component comprising a first air pump 3 and a first air bag 5, the first air bag 5 being arranged at the top of the accommodating cavity 2, and the first air pump 3 being used for inflating the first air bag 5;

[0186] A second inflatable component, the second inflatable component comprises a second air pump 4 and a second air bag 6, the second air bag 6 is arranged at the bottom of the accommodating cavity 2, and the second air pump 4 is used to inflate the first air bag 5;

[0187] A pressure detection component 7, which is disposed on the first airbag 5 and is used to perform pressure detection on a wrist to be measured placed in the accommodating cavity 2;

[0188] The control module 8, the first air pump 3, the second air pump 4 and the pressure detection component 7 are all electrically connected to the control module 8, and the control module 8 is used to control the operation of the first air pump 3 and the second air pump 4 according to the received measurement control signal to expand the first airbag 5 and the second airbag 6, thereby enabling the pressure detection component 7 arranged at the first airbag 5 to detect the pulse of the user's wrist.

[0189] Specifically, the user places the wrist to be measured in the accommodating chamber 2 of the pulse diagnosis monitoring system. The accommodating chamber 2 is composed of an upper shell 11, a main shell 12 and a lower shell 13, forming a closed space for convenient pulse detection.

[0190] When the user starts the measurement, the control module 8 receives the measurement control signal. The control module 8 then starts the first air pump 3 and the second air pump 4. The first air pump 3 inflates the first air bag 5 disposed at the top of the accommodating chamber 2, so that it expands and fits tightly above the user's wrist. The second air pump 4 inflates the second air bag 6 disposed at the bottom of the accommodating chamber 2, so that it expands and supports the bottom of the user's wrist, ensuring that the wrist remains stable during the measurement process.

[0191] As the first airbag 5 and the second airbag 6 expand, they together provide a stable and uniform contact surface for the user's wrist. The pressure detection component 7 arranged on the first airbag 5 starts to work and performs pressure detection on the user's wrist. In this process, the pressure detection component 7 can capture the tiny pressure changes caused by the pulse beating. The pressure detection component 7 transmits the detected pressure signal to the control module 8. The control module 8 processes and analyzes the received signal and extracts relevant information of the pulse (such as heart rate, pulse strength, etc.).

[0192] Finally, the control module 8 may display this information on a user interface, or transmit the data to an external device via other means (such as wireless transmission).

[0193] In the embodiment of the present invention, by inflating the first airbag 5 and the second airbag 6, the system can provide a stable and uniform contact surface for the user's wrist, reducing the influence of external factors on the measurement results. The pressure detection component 7 can capture slight pressure changes, thereby improving the accuracy of pulse detection. The design of the accommodating chamber 2 allows the user's wrist to remain stable during the measurement process, reducing the error caused by wrist movement. The system is easy to operate, and the user only needs to place the wrist in the accommodating chamber 2 and start the measurement. The system can be suitable for people of different ages and body shapes, because the inflation degree of the first airbag 5 and the second airbag 6 can be adjusted as needed.

[0194] More preferably, the number of the first airbags 5 is two, and the two first airbags 5 are symmetrically arranged on the top of the accommodating cavity 2; the pressure detection component 7 is a flexible pressure sensor, and the two ends of the flexible pressure sensor are respectively connected to the two first airbags 5;

[0195] The flexible pressure sensor is provided with a first pressure sensor group and a second pressure sensor group, the first pressure sensor group includes a first pressure detection area 71, a second pressure detection area 72 and a third pressure detection area 73 arranged in sequence longitudinally, and the second pressure sensor group includes a fourth pressure detection area 74, a fifth pressure detection area 75 and a sixth pressure detection area 76 arranged in sequence longitudinally; and each pressure detection area includes a plurality of pressure detection points arranged in sequence transversely;

[0196] During the first pressure sensor detection process, pressure change detection is performed on the corresponding position of the user's wrist through the first pressure detection area 71, the second pressure detection area 72 and the third pressure detection area 73 respectively. During the second pressure sensor detection process, pressure change detection is performed on the corresponding position of the user's wrist through the fourth pressure detection area 74, the fifth pressure detection area 75 and the sixth pressure detection area 76 respectively.

[0197] More preferably, the number of the first airbags 5 is two, and the two first airbags 5 are symmetrically arranged on the top of the accommodating cavity 2; the pressure detection component 7 is a flexible pressure sensor, and the two ends of the flexible pressure sensor are respectively connected to the two first airbags 5;

[0198] The flexible pressure sensor is provided with a first pressure sensor group and a second pressure sensor group, the first pressure sensor group includes a first pressure detection area 71, a second pressure detection area 72 and a third pressure detection area 73 arranged in sequence longitudinally, and the second pressure sensor group includes a fourth pressure detection area 74, a fifth pressure detection area 75 and a sixth pressure detection area 76 arranged in sequence longitudinally; and each pressure detection area includes a plurality of pressure detection points arranged in sequence transversely;

[0199] During the first pressure sensor detection process, pressure change detection is performed on the corresponding position of the user's wrist through the first pressure detection area 71, the second pressure detection area 72 and the third pressure detection area 73 respectively. During the second pressure sensor detection process, pressure change detection is performed on the corresponding position of the user's wrist through the fourth pressure detection area 74, the fifth pressure detection area 75 and the sixth pressure detection area 76 respectively.

[0200] In the embodiment of the present invention, by providing two first airbags 5 symmetrically at the top of the accommodating cavity 2 and a flexible pressure sensor connected thereto, the system can more comprehensively cover the area above the user's wrist. This design helps to capture the pressure changes at different parts of the wrist, thereby more accurately reflecting the detailed characteristics of the pulse.

[0201] The first pressure sensor group and the second pressure sensor group set on the flexible pressure sensor respectively include multiple pressure detection areas arranged longitudinally, and each area includes multiple pressure detection points arranged transversely. This layout enables the system to capture the pressure changes at multiple points on the wrist, further improving the accuracy and comprehensiveness of the measurement. Due to the design of the flexible pressure sensor, it can adapt to users with different wrist shapes and sizes, ensuring close contact at all times during the measurement process. This adaptability helps to reduce measurement errors caused by differences in wrist shape. By setting multiple pressure detection areas and detection points, the system can adjust the range and accuracy of the measurement as needed. For example, when more refined pulse characteristics are required, more or more dense detection points can be focused on; when more extensive pulse information is required, data from multiple detection areas can be integrated. The use of flexible pressure sensors makes the measurement process more comfortable because it can fit the curve of the wrist and reduce the discomfort caused by contact with hard objects.

[0202] Since the system can capture pressure changes at multiple points, it provides rich information for subsequent data analysis. For example, by analyzing the data of different detection areas and detection points, the pulse characteristics of different parts of the wrist can be evaluated, thus providing a more comprehensive basis for TCM diagnosis.

[0203] Flexible sensors are used to make the sensor fit the skin as closely as possible to increase the effective contact points. The average wrist width of an adult is 5.5cm-7.5cm. To simulate the pulse-taking technique, the middle position is taken as the Guan pulse, which fits the inner side of the high bone behind the palm to simulate the width of three fingers of an average adult (about 5cm). Three-section sensors are designed for Cun, Guan, and Chi, and the size of each section is 28.5mm*7mm. Each section uses 16 contacts, which is the maximum number of points under the current width.

[0204] The embodiment of the present invention has three layers of airbags designed at the bottom, and uses an air pump to inflate the airbags, lift the wrist, and allow the wrist to fully contact the upper sensor. A split double-layer airbag is designed above the sensor to solve two problems: the sensor can move downward to a certain distance, so that the wrist position can be fully clamped; the airbags on both sides allow the sensor to fit the radial artery position of the wrist, ensuring that the pulse beat position and the sensor can fully fit.

[0205] More preferably, the pulse diagnosis monitoring system further comprises a background server, a communication module and a camera module, wherein the camera module is used to transmit the monitored user facial expression and camera attributes to the background server through the communication module, wherein the camera attributes include camera number information and camera timestamp; the control module 8 or the background server is used to execute the pulse analysis method based on the pulse diagnosis instrument as described in any one of the first aspects of the embodiments of the present invention;

[0206] The backend server is used to determine the corresponding pulse diagnosis instrument number according to the camera attributes and the camera-pulse diagnosis instrument mapping table, and the backend server is also used to recognize the user's facial expression to determine the user's current emotional state, and update the user's pulse data according to the current emotional state;

[0207] The control module 8 is used to control the first air pump 3 and the second air pump 4 to work according to the first control parameter, the second control parameter and the third control parameter respectively according to the set three-level control mode;

[0208] And / or, the flexible pressure sensor includes 96 pressure monitoring points, each pressure detection area includes 16 pressure detection points arranged in sequence laterally, and the lengths of the first pressure detection area 71, the second pressure detection area 72, the third pressure detection area 73, the fourth pressure detection area 74, the fifth pressure detection area 75 and the sixth pressure detection area 76 are any value between 26 mm and 30 mm, and the widths are any value between 6 mm and 8 mm.

[0209] The solution of the embodiment of the present invention realizes the functions of remote monitoring and data transmission by introducing a background server, a communication module and a camera module, which not only improves the convenience of monitoring, but also makes data analysis and processing more intelligent.

[0210] The backend server can quickly and accurately determine the corresponding pulse diagnosis instrument number based on the camera attributes (such as camera number, timestamp) and the camera-pulse diagnosis instrument mapping table, thereby realizing the accurate association and management of the user's pulse data. The camera module can capture the user's facial expressions and perform emotion recognition through the backend server. This function enables the system to comprehensively consider the impact of the user's emotional state on the pulse data, thereby providing more personalized health analysis and suggestions. According to the user's current emotional state, the system can update the pulse data to reflect the potential impact of emotional changes on the pulse. This helps doctors or users to understand their health status more comprehensively.

[0211] The control module 8 of the embodiment of the present invention provides a three-level control mode, which allows different control parameters (such as the first control parameter, the second control parameter, and the third control parameter) to be selected according to actual needs to control the operation of the first air pump 3 and the second air pump 4. This design improves the flexibility and adaptability of the system and can meet the needs of different users or different application scenarios. The flexible pressure sensor includes 96 pressure monitoring points, and each pressure detection area includes 16 laterally arranged pressure detection points. This refined layout enables the system to capture more subtle pulse changes and improves the accuracy and sensitivity of the measurement. At the same time, the size of each pressure detection area is reasonably designed (the length is 26mm to 30mm, and the width is 6mm to 8mm), which not only ensures the coverage area of ​​the measurement, but also avoids inaccurate measurements caused by excessive size.

[0212] The pulse diagnosis monitoring system described above integrates the background server, communication module and camera module to realize functions such as remote monitoring, intelligent data processing and comprehensive analysis of emotional state and pulse data. At the same time, the system also provides a flexible three-level control method and a refined layout of pressure monitoring points, further improving the accuracy and adaptability of the measurement. The enhancement and expansion of these technical effects enable the pulse diagnosis monitoring system to better serve the field of traditional Chinese medicine diagnosis and health analysis.

[0213] In the embodiment of the present invention, the pressure detection component on the pulse diagnosis instrument can accurately capture the pressure change signal of the user's pulse within the set time, and then process and obtain the original pulse waveform signal. This ensures the accuracy and integrity of the pulse data and provides a reliable basis for subsequent analysis. The user's pulse diagram is input into the pre-built standard pulse model, and the user's pulse type result can be automatically determined, realizing the intelligence and automation of pulse analysis.

[0214] Embodiment 3

[0215] See also Fig.13 , Fig.13 Schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. The electronic device may be a computer, a server, etc. Of course, in certain circumstances, it may also be a smart device such as a mobile phone, a tablet computer, a monitoring terminal, and an image acquisition device with processing functions. Fig.13 As shown, the electronic device may include:

[0216] A memory 510 storing executable program codes;

[0217] a processor 520 coupled to the memory 510;

[0218] The processor 520 calls the executable program code stored in the memory 510 to execute part or all of the steps in the pulse analysis method based on the pulse diagnosis instrument in the first embodiment.

[0219] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute part or all of the steps in the pulse analysis method based on a pulse diagnosis instrument in the first embodiment.

[0220] The embodiment of the present invention further discloses a computer program product, wherein when the computer program product is run on a computer, the computer is enabled to execute part or all of the steps in the pulse analysis method based on the pulse diagnosis instrument in the first embodiment.

[0221] An embodiment of the present invention further discloses an application publishing platform, wherein the application publishing platform is used to publish a computer program product, wherein when the computer program product runs on a computer, the computer executes part or all of the steps in the pulse analysis method based on the pulse diagnosis instrument in embodiment one.

[0222] In various embodiments of the present invention, it should be understood that the size of the serial numbers of the processes does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0223] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., they may be located in one place or distributed over multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0224] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0225] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several requests for a computer device (which can be a personal computer, a server or a network device, etc., specifically a processor in a computer device) to perform some or all of the steps of the method described in each embodiment of the present invention.

[0226] In the embodiments provided by the present invention, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0227] A person of ordinary skill in the art can understand that some or all of the steps in the various methods of the embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0228] The pulse analysis method, system, electronic device and storage medium based on the pulse diagnosis instrument disclosed in the embodiments of the present invention are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A pulse analysis method based on a pulse diagnosis instrument, characterized in that: include: Acquire a pressure change signal detected by a pressure detection component provided at the pulse diagnosis instrument within a set time, and process the pressure change signal within the set time to obtain an original pulse waveform signal associated with the user; Decomposing the original pulse waveform signal according to the selected wavelet basis function and decomposition scale parameter to obtain a first pulse frequency signal and a second pulse frequency signal at different decomposition scales; wherein the first pulse frequency signal is used to characterize a high-frequency detail signal, the high-frequency detail signal includes high-frequency white noise and power frequency noise, and the second pulse frequency noise is used to characterize a low-frequency approximation signal; According to the set noise threshold, the first pulse frequency signal is subjected to high-frequency denoising to remove high-frequency white noise and power frequency noise in the high-frequency detail signal; all wavelet coefficients associated with the high-frequency detail signal are acquired, all wavelet coefficients are traversed, and the absolute value of the wavelet coefficient at a certain point is determined to be greater than the absolute value of its adjacent wavelet coefficient, and the point is used as a signal mutation point; Determine the pulse cycle characteristic value according to all signal mutation points, determine the user pulse data within a set time range according to the pulse cycle characteristic value, and generate a corresponding user pulse diagram based on the user pulse data within the set time range; The obtained user pulse diagram is input into a pre-built standard pulse model to determine the user's pulse type result, and the user's pulse type result is output.

2. The pulse analysis method based on the pulse diagnosis instrument as claimed in claim 1, characterized in that: The user pulse image diagram is generated based on the user pulse data within the set time range, including: Acquire user pulse data within a set time range, and construct a cubic polynomial equation for each user pulse data, wherein the cubic polynomial equation is the same at each data point and the first-order derivative and the second-order derivative between adjacent intervals are continuous; The obtained cubic polynomial equation is calculated to obtain parameter values ​​of each coefficient in the cubic polynomial equation, and a pulse fitting curve is constructed on the interval of each data point according to the calculated parameter values ​​of each coefficient in the cubic polynomial equation; According to the error values ​​between the user's pulse data and the pulse fitting curve, if all the error values ​​are within an error threshold, the pulse fitting curve is determined as the user's pulse diagram.

3. The pulse analysis method based on the pulse diagnosis instrument as claimed in claim 1, characterized in that: After all the wavelet coefficients associated with the high-frequency detail signal are acquired, the method further includes: All wavelet coefficients are screened according to a preset extreme value screening threshold, and all wavelet coefficients are traversed for the screened wavelet coefficients to determine that the absolute value of the wavelet coefficient at a certain point is greater than the absolute value of its adjacent wavelet coefficient; And / or, receiving detection attribute information input by a user, where the detection attribute information includes user gender, detection time and detection season.

4. The pulse analysis method based on the pulse diagnosis instrument as claimed in claim 1, characterized in that: Before obtaining the pressure change signal detected by the pressure detection component provided at the pulse diagnosis instrument within a set time, the method further includes: Receive an arm measurement signal input by a user, and if the arm measurement signal is a left hand signal, start the first pressure sensor group to perform pressure detection on the corresponding area of ​​the user's wrist; if the arm measurement signal is a right hand signal, start the second pressure sensor group to perform pressure detection on the corresponding area of ​​the user's wrist; The method of obtaining a pressure change signal detected by a pressure detection component provided at the pulse diagnosis instrument within a set time, and processing the pressure change signal within the set time to obtain an original pulse waveform signal associated with the user, further includes: When the inflatable component reaches the first set state, the pressure change signals detected by the corresponding three pressure detection areas in the pressure detection component set at the pulse diagnosis instrument within the set time are obtained. If the pressure change signals meet the set requirements, the pressure change signals detected by the corresponding pressure detection areas are processed respectively to obtain the original pulse waveform signals associated with the user; the number of the original pulse waveform signals is three; if the pressure change signals do not meet the set requirements, the inflatable component is controlled to work according to the set adjustment state to obtain the waveform signal; wherein the pressure value in the set adjustment state is greater than the pressure value in the first set state; When the inflatable component reaches the second set state, the pressure change signals detected by the corresponding three pressure detection areas in the pressure detection component provided at the pulse diagnosis instrument within the set time are obtained, and the pressure change signals detected by the corresponding pressure detection areas are processed respectively to obtain the original pulse waveform signals associated with the user; the number of the original pulse waveform signals is three; When the inflation component reaches the third set state, the pressure change signals detected within the set time by the corresponding three pressure detection areas in the pressure detection component provided at the pulse diagnosis instrument are obtained, and the pressure change signals detected by the corresponding pressure detection areas are processed respectively to obtain original pulse waveform signals associated with the user; the number of the original pulse waveform signals is three.

5. The pulse analysis method based on the pulse diagnosis instrument as claimed in claim 4, characterized in that: The standard pulse model is obtained by the following steps: Acquire a sample training set, the sample training set comprising pulse sample images; and divide the sample training set into a training set, a validation set and a test set; Performing a preprocessing operation on the pulse sample image; Inputting the pulse sample image after the preprocessing operation into the standard pulse model with completed parameter configuration for training and parameter configuration adjustment until the corresponding standard pulse model meets the training requirements, and saving the current optimal model parameters; the standard pulse model is a model constructed based on a convolutional neural network; Use the trained standard pulse model to predict the pulse test images in the test set to obtain the pulse type to which each pulse test image belongs; Generate a category confusion matrix based on the prediction results and true annotations of the standard pulse model; the rows of the category confusion matrix represent true categories, and the columns represent predicted categories; Calculating overall performance indicators of the standard pulse model according to the category confusion matrix, wherein the overall performance indicators include accuracy, precision, recall rate and F1 score; And according to the analysis result of the type confusion matrix, the standard pulse model is adjusted and optimized; The method of inputting the obtained user pulse diagram into a pre-built standard pulse model to determine the user's pulse type result comprises: The obtained user pulse diagram is calculated with the pre-built standard pulse model by using the Euclidean distance formula to obtain the similarity value with each standard pulse, and the maximum value among the similarity values ​​with each standard pulse is determined. If the maximum value among the similarity values ​​of each standard pulse model is greater than the similarity threshold, the user's pulse type result is determined.

6. The pulse analysis method based on the pulse diagnosis instrument as claimed in claim 4, characterized in that: The pulse analysis method further comprises: Obtain pressure change signals detected by corresponding three pressure detection areas in the pressure detection assembly provided at the pulse diagnosis instrument within a set time, and determine corresponding pulse position data according to the pressure change signals detected by corresponding three pressure detection areas within a set time; The user's pulse diagram is identified through a waveform recognition algorithm to determine the corresponding pulse beat cycle, the amplitude of the Cun, Guan, and Chi waveforms, pulse width data, the interval time between each beat, the strength of the pulse fluctuation, the smoothness of the pulse fluctuation, the tension of the pulse, and the uniformity of the waveform; Determine the corresponding pulse type based on the obtained pulse cycle, the amplitude of the Cun, Guan, and Chi waveforms, the pulse width data, the interval time between each beat, the strength of the pulse fluctuation, the smoothness of the pulse fluctuation, the tension of the pulse, and the uniformity of the waveform; Determine the yang index, moisture index, qi and blood index, and blood stasis index of the corresponding user according to the pulse type and pulse-index mapping association; The obtained Yang Qi index, moisture index, Qi and blood index and blood stasis index are input into the Zang-Fu calculation formula to obtain the corresponding Zang-Fu scores; the Zang-Fu calculation formula is: Organ score = ∑(pulse condition * similarity * (yang index + dampness index + qi and blood index + blood stasis index)); The meridian score is obtained based on the comprehensive data of pulse condition, viscera and constitution and the meridian calculation formula, wherein the meridian calculation formula is: meridian score = ∑ (pulse condition * similarity + constitution dialectics * weight + viscera dialectics * weight); Outputting the pulse type, organ score and meridian score to the corresponding user terminal; And / or, after determining the pulse cycle characteristic value according to all signal mutation points, determining the user pulse data within the set time range according to the pulse cycle characteristic value, and generating a corresponding user pulse diagram based on the user pulse data within the set time range, further comprising: Extracting key pulse characteristic parameters from the collected pulse waveform data, the pulse characteristic parameters including the shape and position of the main wave, the dicrotic wave, the descending isthmus, as well as the pulse period, frequency, amplitude, peak-to-peak value, rise time, and fall time; Performing preprocessing operations on the extracted feature parameters, wherein the preprocessing operations include data cleaning, normalization and dimensionality reduction operations; The pulse characteristic parameters of the newly collected pulse waveform data are input as observation data into the Bayesian model, and the Bayesian model calculates the posterior probabilities of various pulse types based on the prior knowledge base and the observation data, and outputs the most likely pulse type as the recognition result based on the calculated posterior probabilities; After the pulse type result of the user is determined, the method further comprises: Combining the recognition results of the Bayesian model and the results of waveform similarity recognition, a comprehensive judgment is made. If the conclusions drawn by the two are consistent or similar, the final pulse type can be determined.

7. The pulse analysis method based on the pulse diagnosis instrument as claimed in claim 4, characterized in that: The pulse analysis method further comprises: Obtain the user's facial image at each time detection point captured by the camera; Inputting the user's facial image at each time detection point into a pre-built facial analysis model for recognition to determine the user's emotion type at each time detection point; wherein the facial analysis model is constructed by a convolutional neural network; The emotion type of the user at each time detection point is input into a waveform modification model constructed based on the relationship between emotion and pulse to optimize the pulse parameters so as to perform a correction operation on the obtained user pulse diagram.

8. A pulse diagnosis monitoring system, characterized in that: include: A housing component, wherein the housing component is provided with a receiving cavity, wherein the receiving cavity is used to receive the wrist to be measured, A first inflatable component, the first inflatable component includes a first air pump and a first air bag, the first air bag is arranged on the top of the accommodating cavity, and the first air pump is used to inflate the first air bag; a second inflatable component, the second inflatable component comprising a second air pump and a second air bag, the second air bag being arranged at the bottom of the accommodating cavity, and the second air pump being used to inflate the first air bag; A pressure detection component, the detection component is arranged on the first airbag, and the detection component is used to perform pressure detection on a wrist to be measured placed in the accommodating cavity; A control module, wherein the first air pump, the second air pump and the pressure detection component are all electrically connected to the control module, and the control module is used to control the first air pump and the second air pump to operate according to the received measurement control signal so as to expand the first airbag and the second airbag, thereby enabling the pressure detection component arranged at the first airbag to detect the pulse of the user's wrist.

9. The pulse diagnosis monitoring system according to claim 8, characterized in that: The number of the first airbags is two, and the two first airbags are symmetrically arranged on the top of the accommodating cavity; the pressure detection component is a flexible pressure sensor, and the two ends of the flexible pressure sensor are respectively connected to the two first airbags; The flexible pressure sensor is provided with a first pressure sensor group and a second pressure sensor group, the first pressure sensor group includes a first pressure detection area, a second pressure detection area and a third pressure detection area arranged in sequence longitudinally, and the second pressure sensor group includes a fourth pressure detection area, a fifth pressure detection area and a sixth pressure detection area arranged in sequence longitudinally; and each pressure detection area includes a plurality of pressure detection points arranged in sequence transversely; During the first pressure sensor detection process, pressure changes in corresponding positions of the user's wrist are detected through the first pressure detection area, the second pressure detection area and the third pressure detection area respectively. During the second pressure sensor detection process, pressure changes in corresponding positions of the user's wrist are detected through the fourth pressure detection area, the fifth pressure detection area and the sixth pressure detection area respectively.

10. The pulse diagnosis monitoring system according to claim 8, characterized in that: The pulse diagnosis monitoring system further includes a background server, a communication module and a camera module, wherein the camera module is used to transmit the monitored user facial expression and camera attributes to the background server through the communication module, wherein the camera attributes include camera number information and camera timestamp; the control module or the background server is used to execute the pulse analysis method based on the pulse diagnosis instrument according to any one of claims 1 to 7; The backend server is used to determine the corresponding pulse diagnosis instrument number according to the camera attributes and the camera-pulse diagnosis instrument mapping table, and the backend server is also used to recognize the user's facial expression to determine the user's current emotional state, and update the user's pulse data according to the current emotional state; The control module is used to control the first air pump and the second air pump to work according to the first control parameter, the second control parameter and the third control parameter respectively according to the set three-level control mode; And / or, the flexible pressure sensor includes 96 pressure monitoring points, each pressure detection area includes 16 pressure detection points arranged in sequence laterally, and the length of the first pressure detection area, the second pressure detection area, the third pressure detection area, the fourth pressure detection area, the fifth pressure detection area and the sixth pressure detection area is any value between 26 mm and 30 mm, and the width is any value between 6 mm and 8 mm.

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