A remote diagnosis and treatment method and system based on a pulse simulator

Abstract

The present invention discloses a remote diagnosis and treatment method and system based on a pulse simulator, which relates to the technical field of medical care. The system discloses a patient pulse acquisition module, a pulse parameter simulation setting module, a pulse dimension diagnosis module, and a pulse remote diagnosis and treatment module. By setting up the patient pulse acquisition module, the pulse parameter simulation setting module, the pulse dimension diagnosis module, and the pulse remote diagnosis and treatment module, the pulse signal of the patient can be simulated periodically through the pulse simulator, ensuring the diagnostic accuracy of the pulse diagnosis for the patient. It is possible to preferentially diagnose and analyze the potentially abnormal pulse dimension data of the patient, and there is no longer a need to analyze the pulse data of all dimensions simultaneously. This can not only improve the processing efficiency of the pulse data for the pulse diagnosis of the patient, but also effectively reduce the processing difficulty of the pulse data for the pulse diagnosis of the patient.

Description

Technical Field

[0001] The present invention relates to the technical field of healthcare, and more specifically, it relates to a remote diagnosis and treatment method and system based on a pulse simulator. Background Art

[0002] In traditional Chinese medicine diagnosis and treatment, pulse diagnosis is an indispensable and important link. However, due to geographical restrictions and uneven distribution of medical resources, many patients are difficult to directly meet experienced Chinese medicine doctors for pulse diagnosis. With the progress of medical standards, remote pulse measurement devices have gradually been introduced. The remote pulse measurement devices can collect the pulse signals of patients and remotely send the pulse signals to doctors waiting in line for diagnosis and treatment. However, a large number of normal pulse signals are mixed in the diagnosis and treatment queue, which will cause patients with abnormal pulses to not be able to receive effective diagnosis and treatment in time, and at the same time cause the doctors' diagnosis and treatment pressure to become greater and greater. And the remote pulse measurement device needs to analyze all dimensions of the pulse data before it can analyze whether there is an abnormality in the patient's pulse. This efficiency of pulse analysis is not high. Summary of the Invention

[0003] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a remote diagnosis and treatment method and system based on a pulse simulator.

[0004] To achieve the above purpose, the present invention provides the following technical solutions:

[0005] A remote diagnosis and treatment method based on a pulse simulator, comprising the following steps:

[0006] Step 1: Collect the pulse signals of the patient;

[0007] Step 2: Periodically obtain the simulation parameters of various types of pulse parameters of the patient;

[0008] Step 3: After obtaining the simulation parameters of various types of pulse parameters of the patient, input the simulation parameters of various types of pulse parameters into the pulse simulator;

[0009] Step 4: Set the diagnosis order of the pulse dimension data of the patient;

[0010] Step 5: After diagnosing each pulse dimension data, generate a dimension diagnosis record of the pulse dimension data;

[0011] Step 6: Whenever a dimension diagnosis record is generated, obtain the remote diagnosis and treatment value of the patient, and based on the comparison result between the remote diagnosis and treatment value and its threshold, determine whether to mark the patient as a pulse diagnosis and treatment patient;

[0012] Step 7: When the patient is marked as a pulse diagnosis and treatment patient, register the pulse diagnosis and treatment patient to the terminal of the remote diagnosis and treatment doctor.

[0013] Further, a remote diagnosis and treatment system based on a pulse simulator includes a patient pulse acquisition module, a pulse parameter simulation setting module, a pulse dimension diagnosis module, and a pulse remote diagnosis and treatment module;

[0014] The patient pulse acquisition module is used to acquire the pulse signal of the patient;

[0015] The pulse parameter simulation setting module is used to periodically obtain the simulation parameters of various types of patient pulse parameters;

[0016] After obtaining the simulation parameters of various types of patient pulse parameters, the pulse dimension diagnosis module inputs the simulation parameters of various types of pulse parameters into the pulse simulator, and at the same time sets the diagnosis order of the pulse dimension data of the patient. After diagnosing each pulse dimension data, a dimension diagnosis record of the pulse dimension data is generated, and then the next pulse dimension data is diagnosed according to the diagnosis order of the pulse dimension data;

[0017] Whenever a dimension diagnosis record is generated, the pulse remote diagnosis and treatment module obtains the remote diagnosis and treatment value of the patient, and based on the comparison result between the remote diagnosis and treatment value and its threshold, determines whether to mark the patient as a pulse diagnosis and treatment patient. When the patient is marked as a pulse diagnosis and treatment patient, the pulse diagnosis and treatment patient is registered to the terminal of the remote diagnosis and treatment doctor.

[0018] Further, periodically obtaining the simulation parameters of various types of patient pulse parameters specifically includes: based on a preset period, when reaching the period node, collecting all the pulse signals of the patient within the period, obtaining the actual parameters of various types of pulse parameters according to the pulse signals, summing up the actual parameters of the same type of pulse parameters and taking the average value to obtain the simulation parameters of this type of pulse parameter, and further obtaining the simulation parameters of various types of patient pulse parameters.

[0019] Further, the dimension diagnosis record includes the patient name, the pulse dimension data name, the dimension diagnosis index, and the dimension diagnosis time.

[0020] Further, the dimension diagnosis index of the dimension diagnosis record is obtained through the following method: obtaining the pulse signal of the pulse simulator, synchronously obtaining the pulse dimension index model of the pulse dimension data, and inputting the pulse signal into the pulse dimension index model to obtain the dimension diagnosis index of the pulse dimension data.

[0021] Further, the remote diagnosis value of the patient is obtained in the following manner: Obtain all abnormal pulse condition dimension data and all normal pulse condition dimension data of the patient in this cycle. Analyze all pairs of abnormal pulse condition dimension data. When an association line is shown between two abnormal pulse condition dimension data in the pulse condition dimension data spectrum, sum up the dimension diagnosis indices of the two abnormal pulse condition dimension data and take the average to obtain the associated abnormal balance index. When no association line is shown between two abnormal pulse condition dimension data in the pulse condition dimension data spectrum, increase the abnormal pulse condition count by one. Sum up all the associated abnormal balance indices and take the average to obtain the associated abnormal balance average index Fgc. Sum up all the abnormal pulse condition counts to obtain the abnormal pulse condition sum count Rew. Mark the total quantity of abnormal pulse condition dimension data as Ty and the total quantity of normal pulse condition dimension data as Sy. Use the formula to obtain the remote diagnosis value Fs of the patient, where z1 is the coefficient of the associated abnormal balance average index, z2 is the coefficient of the abnormal pulse condition sum count, z3 is the coefficient of the quantity of abnormal pulse condition dimension data, and z4 is the coefficient of the quantity of normal pulse condition dimension data.

[0022] Further, the abnormal pulse condition dimension data and the normal pulse condition dimension data are obtained in the following manner: Whenever a dimension diagnosis record is generated, obtain the name of the pulse condition dimension data and the dimension diagnosis index of this dimension diagnosis record. Obtain the dimension diagnosis boundary index of this pulse condition dimension data. When the dimension diagnosis index is greater than the dimension diagnosis boundary index, mark this pulse condition dimension data as abnormal pulse condition dimension data. When the dimension diagnosis index is less than or equal to the dimension diagnosis boundary index, mark this pulse condition dimension data as normal pulse condition dimension data.

[0023] Further, the diagnosis order of the patient's pulse condition dimension data is obtained in the following manner:

[0024] Obtain all dimension diagnosis records of the patient before the current time of the system. Obtain the name of the pulse condition dimension data of the dimension diagnosis record. Mark the dimension diagnosis records with the same pulse condition dimension data name as the same-pulse dimension diagnosis records. Sum up the dimension diagnosis indices of all the same-pulse dimension diagnosis records and take the average to obtain the average dimension diagnosis index TSD. Sort all the same-pulse dimension diagnosis records in chronological order of the dimension diagnosis time. Calculate the difference between the dimension diagnosis indices of two adjacent same-pulse dimension diagnosis records after sorting and take the absolute value to obtain the dimension diagnosis floating rise and fall value. Set the dimension diagnosis floating rise and fall threshold. When the dimension diagnosis floating rise and fall value is greater than or equal to the dimension diagnosis floating rise and fall threshold, increase the abnormal floating rise and fall count by one. Sum up all the abnormal floating rise and fall counts and take the average to obtain the abnormal floating rise and fall sum count SKL. Sum up all the dimension diagnosis floating rise and fall values and take the average to obtain the dimension diagnosis floating rise and fall average value EW. Use the formula Obtain the diagnostic sequence value Rg of the pulse dimension data, where y1 is the average dimension diagnostic index coefficient, y2 is the coefficient of abnormal floating and falling and frequency, and y3 is the average coefficient of floating and falling in dimension diagnosis. Sort all the pulse dimension data in descending order according to the value of the diagnostic sequence value, and then generate the diagnostic order of the patient's pulse dimension data.

[0025] Furthermore, set a remote diagnosis threshold. When the remote diagnosis value is greater than or equal to the remote diagnosis threshold, mark the patient as a pulse diagnosis patient, obtain the basic information of the patient and the simulated pulse signal of the pulse simulator, and register the pulse diagnosis patient to the terminal of the remote diagnosis doctor. When the remote diagnosis value is less than the remote diagnosis threshold, no processing is performed.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. The method of the present invention finely diagnoses the pulse through the pulse data of each dimension. During the diagnosis process, the pulse diagnosis patients are marked and registered in a timely manner, effectively improving the remote diagnosis efficiency of the patient's pulse.

[0028] 2. Set up a patient pulse acquisition module, a pulse parameter simulation setting module, a pulse dimension diagnosis module, and a pulse remote diagnosis module. The pulse signal of the patient can be simulated periodically through the pulse simulator, ensuring the diagnosis accuracy of the patient's pulse. The potentially abnormal pulse dimension data of the patient can be preferentially diagnosed and analyzed, and it is no longer necessary to analyze all the pulse dimension data at the same time. This can not only improve the processing efficiency of the pulse data for diagnosing the patient's pulse, but also effectively reduce the processing difficulty of the pulse data for diagnosing the patient's pulse. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a flowchart of a remote diagnosis method based on a pulse simulator;

[0030] Figure 2 is a block diagram of a module of a remote diagnosis system based on a pulse simulator;

[0031] Figure 3 is a flowchart of obtaining abnormal pulse dimension data and normal pulse dimension data. DETAILED DESCRIPTION OF THE INVENTION

[0032] Example 1

[0033] Refer to Figure 1 , a remote diagnosis method based on a pulse simulator, includes the following steps:

[0034] Step 1: Collect the pulse signal of the patient;

[0035] Step 2: Periodically obtain the simulation parameters of various types of pulse parameters of the patient;

[0036] Step 3: After obtaining the simulation parameters of various types of pulse parameters of the patient, input the simulation parameters of various types of pulse parameters into the pulse simulator;

[0037] Step 4: Set the diagnosis order of the pulse dimension data of the patient;

[0038] Step 5: After diagnosing each pulse dimension data, generate a dimension diagnosis record for the pulse dimension data;

[0039] Step 6: Whenever a dimension diagnosis record is generated, obtain the remote diagnosis and treatment value of the patient, and based on the comparison result between the remote diagnosis and treatment value and its threshold, determine whether to mark the patient as a pulse diagnosis and treatment patient;

[0040] Step 7: When the patient is marked as a pulse diagnosis and treatment patient, register the pulse diagnosis and treatment patient to the terminal of the remote diagnosis and treatment doctor.

[0041] The method of the present invention finely diagnoses the pulse through the pulse data of each dimension. During the diagnosis process, the pulse diagnosis and treatment patients are marked and registered in a timely manner, effectively improving the efficiency of remote diagnosis and treatment of the patient's pulse.

[0042] Embodiment 2

[0043] Refer to Figures 2 - 3 , a remote diagnosis and treatment system based on a pulse simulator, including a patient pulse acquisition module, a pulse parameter simulation setting module, a pulse dimension diagnosis module, and a pulse remote diagnosis and treatment module.

[0044] The patient pulse acquisition module is used to collect the pulse signal of the patient (the patient is equipped with a pulse acquisition device, and the patient can collect his own pulse signal through the pulse acquisition device. Each collection can obtain a pulse signal, and the collection frequency can be adjusted according to the doctor's instructions or the patient's needs, which is not the focus of this application).

[0045] The pulse parameter simulation setting module is based on a preset period (the period node interval of the preset period can be adjusted according to actual needs). When the period node is reached, all the pulse signals of the patient within this period (between the current period node and the previous period node) are collected. According to the pulse signals, the actual parameters of various types of pulse parameters are obtained (the pulse signals can be processed through filtering, amplification, AD conversion, etc., and then the actual parameters of various types of pulse parameters are obtained through time domain analysis, frequency domain analysis, etc. The types of pulse parameters include but are not limited to pulse frequency, blood oxygen saturation, pulse intensity, amplitude of the rising branch of the waveform, slope of the waveform peak, etc.). The actual parameters of the same type of pulse parameters are summed and averaged to obtain the simulation parameters of this type of pulse parameter.

[0046] Pulse Dimension Diagnosis Module: After obtaining the simulation parameters of various types of pulse parameters of the patient, input the simulation parameters of various types of pulse parameters into the pulse simulator (when simulating the pulse signal, the pulse simulator needs to rely on the input simulation parameters). At the same time, set the diagnosis order of the pulse dimension data of the patient, and diagnose each pulse dimension data (pulse dimension data is a key part of traditional Chinese medicine diagnosis, used to reflect the state of qi and blood circulation and health status of the patient. Pulse dimension data includes but is not limited to pulse position, pulse rate, pulse force, pulse length, pulse width, fluency. Pulse position: refers to the position where the pulse appears, such as floating pulse, deep pulse, etc. Pulse shape: refers to the shape of the pulse, such as full pulse, thin pulse, etc. Pulse force: refers to the strength and fluency of the pulse, such as real pulse, deficient pulse, slippery pulse, unsmooth pulse, etc.). After diagnosis, generate a dimension diagnosis record for the pulse dimension data. The dimension diagnosis record includes the patient name (i.e., the name of the patient to whom the pulse dimension data belongs), the pulse dimension data name (i.e., the name of the pulse dimension data in medicine, such as pulse position, pulse rate, pulse force), the dimension diagnosis index, and the dimension diagnosis time. Then continue to diagnose the next pulse dimension data according to the pulse dimension data diagnosis order (until all pulse dimension data in the pulse dimension data diagnosis order are diagnosed).

[0047] The dimension diagnosis index of the dimension diagnosis record is obtained in the following way: Obtain the pulse signal of the pulse simulator, synchronously obtain the pulse dimension index model of the pulse dimension data, and input the pulse signal into the pulse dimension index model to obtain the dimension diagnosis index of the pulse dimension data.

[0048] Whenever a dimension diagnosis record is generated, obtain the pulse dimension data name and the dimension diagnosis index of the dimension diagnosis record, and obtain the dimension diagnosis boundary index of the pulse dimension data (different pulse dimension data correspond to different dimension diagnosis boundary indexes. The dimension diagnosis boundary index is the threshold set by the system and can be modified according to actual needs). When the dimension diagnosis index is greater than the dimension diagnosis boundary index, mark the pulse dimension data as an abnormal pulse dimension data. When the dimension diagnosis index is less than or equal to the dimension diagnosis boundary index, mark the pulse dimension data as a normal pulse dimension data.

[0049] Pulse remote diagnosis and treatment module: Whenever a dimensional diagnosis record is generated, obtain the remote diagnosis and treatment value of the patient, set the remote diagnosis and treatment threshold (the remote diagnosis and treatment threshold is the system-set threshold and can be modified according to actual needs). When the remote diagnosis and treatment value is greater than or equal to the remote diagnosis and treatment threshold, mark the patient as a pulse diagnosis and treatment patient, obtain the basic patient information (such as name, age) of the pulse diagnosis and treatment patient and the simulated pulse signal of the pulse simulator, and register the pulse diagnosis and treatment patient to the terminal of the remote diagnosis and treatment doctor (the terminal of the remote diagnosis and treatment doctor will display the queuing number, basic patient information, and simulated pulse signal of the pulse diagnosis and treatment patient, and then the patient can queue up waiting for the remote diagnosis and treatment doctor to conduct remote diagnosis and treatment on the pulse diagnosis and treatment patient). When the remote diagnosis and treatment value is less than the remote diagnosis and treatment threshold, no processing is performed.

[0050] The remote diagnosis and treatment value of the patient is obtained through the following method: Obtain all abnormal pulse condition dimensional data and all normal pulse condition dimensional data of the patient in this cycle, and analyze all abnormal pulse condition dimensional data in pairs. When an association line is shown between two abnormal pulse condition dimensional data in the pulse condition dimensional data spectrum, sum and average the dimensional diagnosis indexes of the two abnormal pulse condition dimensional data to obtain the associated abnormal balance index. When no association line is shown between two abnormal pulse condition dimensional data in the pulse condition dimensional data spectrum, increase the number of abnormal pulse conditions by one. Sum and average all the associated abnormal balance indexes to obtain the associated abnormal balance average index Fgc. Sum all the numbers of abnormal pulse conditions to obtain the sum of the number of abnormal pulse conditions Rew. Mark the total number of abnormal pulse condition dimensional data as Ty, and mark the total number of normal pulse condition dimensional data as Sy. Use the formula to obtain the remote diagnosis and treatment value Fs of the patient, where z1 is the coefficient of the associated abnormal balance average index, z2 is the coefficient of the sum of the number of abnormal pulse conditions, z3 is the coefficient of the number of abnormal pulse conditions, z4 is the coefficient of the number of normal pulse conditions. The value of z1 is 0.58, the value of z2 is 0.62, the value of z3 is 0.98, and the value of z4 is 0.95.

[0051] The pulse condition dimensional data spectrum contains all pulse condition dimensional data, and shows the association lines between all pulse condition dimensional data in the pulse condition dimensional data spectrum. For example, the change of pulse position will cause the change of pulse force (when the pulse position changes, such as from floating to sinking or from sinking to floating, the pulse force will also change accordingly. For example, when the floating pulse changes to the sinking pulse, the pulse force may increase and the smoothness may also become more uniform; while when the sinking pulse changes to the floating pulse, the force may weaken and the smoothness may become worse), and the change of pulse force will cause the change of pulse position (when the pulse shows strong force and tension, it may mean that the pulse position is deeper; while when the pulse force is weak and the tension is low, it may mean that the pulse position is shallower). Then, the pulse position and pulse force show an association line in the pulse condition dimensional data spectrum (indicating that the pulse position and pulse force affect each other).

[0052] The diagnostic order of the pulse condition dimension data of the patient is obtained through the following method:

[0053] Obtain all dimension diagnosis records of the patient before the current time of the system, obtain the names of the pulse condition dimension data in the dimension diagnosis records, mark the dimension diagnosis records with the same pulse condition dimension data name as the same-pulse dimension diagnosis records, sum up the dimension diagnosis indices of all the same-pulse dimension diagnosis records and take the average to obtain the average dimension diagnosis index TSD, sort all the same-pulse dimension diagnosis records in the chronological order of the dimension diagnosis time, calculate the difference between the dimension diagnosis indices of two adjacent same-pulse dimension diagnosis records after sorting and take the absolute value to obtain the dimension diagnosis floating value, set the dimension diagnosis floating threshold (the dimension diagnosis floating threshold is a threshold set by the system and can be modified according to actual needs). When the dimension diagnosis floating value is greater than or equal to the dimension diagnosis floating threshold, increase the abnormal floating count by one, sum up all the abnormal floating counts and take the average to obtain the abnormal floating sum count SKL, sum up all the dimension diagnosis floating values and take the average to obtain the dimension diagnosis floating average EW, and use the formula to obtain the diagnostic order value Rg of the pulse condition dimension data, where y1 is the average dimension diagnosis index coefficient, y2 is the abnormal floating sum count coefficient, y3 is the dimension diagnosis floating average coefficient. The value of y1 is 2.83, the value of y2 is 3.97, and the value of y3 is 1.98. Sort all the pulse condition dimension data in descending order of the diagnostic order value, and then generate the diagnostic order of the pulse condition dimension data of the patient (for example: for the four pulse condition dimension data of patient a: pulse position, pulse rate, pulse force, and pulse length, where the diagnostic order value of the pulse position > the diagnostic order value of the pulse force > the diagnostic order value of the pulse rate > the diagnostic order value of the pulse length, then the diagnostic order of the pulse condition dimension data of patient a is 1. Pulse position; 2. Pulse force; 3. Pulse rate; 4. Pulse length. First, diagnose the pulse position of patient a, and finally diagnose the pulse length of patient a. In the special case where the diagnostic order values of two pulse condition dimension data are equal, either one can be diagnosed first).

[0054] Each pulse dimension data corresponds to an independent pulse dimension index model. In this embodiment, taking the pulse force as an example, the construction method of the pulse dimension index model will be introduced: p pulse signals are collected, feature extraction is performed on each pulse signal to obtain the pulse feature set of each pulse signal. The pulse feature sets of the p pulse signals are used as the training data of the neural network model. A dimension diagnosis index is assigned to the pulse feature set of each pulse signal. The p training data are divided into a training set and a validation set according to a ratio of 2:3, and the training set and the validation set are iteratively trained by the neural network to obtain the pulse dimension index model. Among them, the larger the value of the dimension diagnosis index, the more abnormal the pulse force; the smaller the value of the dimension diagnosis index, the more normal the pulse force. The construction methods of the pulse dimension index models for the remaining pulse dimension data are the same as the above. The index range of the dimension diagnosis indexes of all pulse dimension index models is (1~2).

[0055] A patient pulse acquisition module, a pulse parameter simulation setting module, a pulse dimension diagnosis module, and a pulse remote diagnosis and treatment module are set up. The pulse signals of the patient can be simulated periodically through a pulse simulator to ensure the diagnostic accuracy of the pulse diagnosis of the patient. The pulse dimension data with potential abnormalities of the patient can be preferentially diagnosed and analyzed, and it is no longer necessary to analyze the pulse data of all dimensions at the same time. This can not only improve the processing efficiency of the pulse data for the pulse diagnosis of the patient, but also effectively reduce the processing difficulty of the pulse data for the pulse diagnosis of the patient.

[0056] The above formulas are all dimensionless and take their numerical calculations. The formula is a formula obtained by software simulation by collecting a large amount of data to approximate the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0057] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that contains one or more sets of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0058] It should be understood that in various embodiments of the present application, the order numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0059] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0060] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0061] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in electrical, mechanical, or other forms.

[0062] If the described functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs, and other various media that can store program codes.

[0063] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claimed rights.

Claims

1. A remote diagnosis and treatment method based on a pulse simulator, characterized in that: The steps include: Step 1: Collect the patient's pulse signal; Step 2: Periodically obtain simulation parameters of various types of pulse parameters of the patient; Periodically obtaining simulation parameters of various types of pulse parameters of the patient, specifically: based on a preset cycle, when a cycle node is reached, all pulse signals of the patient in the cycle are collected, actual parameters of various types of pulse parameters are obtained according to the pulse signals, actual parameters of the same type of pulse parameters are summed and averaged to obtain simulation parameters of the type of pulse parameters, and then simulation parameters of various types of pulse parameters of the patient are obtained; Step 3: After obtaining the simulation parameters of each type of pulse parameter of the patient, input the simulation parameters of each type of pulse parameter into the pulse simulator; Step 4: setting the patient's pulse dimension data diagnostic order; wherein the pulse dimension data includes but is not limited to pulse position, pulse rate, pulse strength, pulse length, pulse width, and fluency; Step 5: After diagnosing each pulse dimension data, generate a dimension diagnosis record of the pulse dimension data; The dimension diagnosis record includes the patient name, pulse dimension data name, dimension diagnosis index, and dimension diagnosis time; The dimensional diagnosis index of the dimensional diagnosis record is obtained by the following method: obtaining the pulse signal of the pulse simulator, synchronously obtaining the pulse dimensional index model of the pulse dimensional data, inputting the pulse signal into the pulse dimensional index model, and obtaining the dimensional diagnosis index of the pulse dimensional data; wherein each pulse dimensional data corresponds to an independent pulse dimensional index model, and the pulse dimensional index model is constructed by: collecting p pulse signals, extracting features from each pulse signal, obtaining a pulse feature set of each pulse signal, using the pulse feature sets of the p pulse signals as training data for the neural network model, assigning a dimensional diagnosis index to the pulse feature set of each pulse signal, dividing the p training data into a training set and a validation set, iteratively training the neural network model, and obtaining a pulse dimensional index model; wherein, the larger the value of the dimensional diagnosis index, the more abnormal the corresponding pulse dimensional data, and the smaller the value of the dimensional diagnosis index, the more normal the corresponding pulse dimensional data; Step 6: Whenever a dimensional diagnosis record is generated, the remote diagnosis value of the patient is obtained, and based on the comparison result between the remote diagnosis value and its threshold, it is determined whether to mark the patient as a pulse diagnosis patient; The remote diagnosis and treatment value of the patient is obtained in the following way: obtain all abnormal pulse dimension data and all normal pulse dimension data of the patient in this period, analyze all abnormal pulse dimension data in pairs, when two abnormal pulse dimension data show a correlation line in the pulse dimension data spectrum, sum and average the dimension diagnosis indexes of the two abnormal pulse dimension data to obtain the associated abnormal balance index, when the two abnormal pulse dimension data do not show a correlation line in the pulse dimension data spectrum, set the number of abnormal pulses to once; after all abnormal pulse dimension data have completed the pairwise analysis, sum and average all associated abnormal balance indices to obtain the associated abnormal balance average index Fgc, sum all abnormal pulse times to obtain the abnormal pulse sum Rew, mark the total number of abnormal pulse dimension data as Ty, mark the total number of normal pulse dimension data as Sy, and use the formula Obtain the patient's remote diagnosis and treatment value Fs, wherein z1 is the correlation abnormal balance index coefficient, z2 is the abnormal pulse and frequency coefficient, z3 is the abnormal pulse quantity coefficient, and z4 is the normal pulse quantity coefficient, wherein the pulse dimension data spectrum contains all pulse dimension data, and the correlation lines between all pulse dimension data are displayed in the pulse dimension data spectrum; The abnormal pulse dimension data and the normal pulse dimension data are obtained in the following manner: whenever a dimension diagnosis record is generated, the pulse dimension data name and the dimension diagnosis index of the dimension diagnosis record are obtained, and the dimension diagnosis limit index of the pulse dimension data is obtained; when the dimension diagnosis index is greater than the dimension diagnosis limit index, the pulse dimension data is marked as abnormal pulse dimension data; when the dimension diagnosis index is less than or equal to the dimension diagnosis limit index, the pulse dimension data is marked as normal pulse dimension data; Step 7: When the patient is marked as a pulse diagnosis and treatment patient, the pulse diagnosis and treatment patient is registered to the remote diagnosis and treatment doctor's terminal.

2. A remote diagnosis and treatment method based on a pulse simulator according to claim 1, characterized in that: The patient's pulse dimension data diagnostic order is obtained in the following way: Get all the dimensional diagnosis records of the patient before the current time of the system, get the pulse dimension data name of the dimensional diagnosis record, mark the dimensional diagnosis records with the same pulse dimension data name as the same pulse dimension diagnosis record; for each pulse dimension data, sum and average the dimensional diagnosis indexes of all the same pulse dimension diagnosis records to get the average dimensional diagnosis index TSD, sort all the same pulse dimension diagnosis records in the chronological order of the dimension diagnosis time, calculate the difference between the dimensional diagnosis indexes of the two adjacent same pulse dimension diagnosis records after sorting and take the absolute value to get the dimensional diagnosis floating drop value, set the dimensional diagnosis floating drop threshold, when the dimensional diagnosis floating drop value is greater than or equal to the dimensional diagnosis floating drop threshold, set the number of abnormal floating drops to one; when all two adjacent same pulse dimensional diagnosis records complete the dimensional diagnosis floating drop value judgment, sum all the abnormal floating drop times to get the abnormal floating drop sum number SKL, sum all the dimensional diagnosis floating drop values ​​and take the average to get the dimensional diagnosis floating drop mean EW, use the formula The diagnostic sequence value Rg of the pulse dimension data is obtained, where y1 is the average dimension diagnostic index coefficient, y2 is the abnormal floating drop and number coefficient, and y3 is the dimension diagnostic floating drop mean coefficient. All pulse dimension data are sorted from large to small according to the value of the diagnostic sequence value, and then the patient's pulse dimension data diagnostic order is generated.

3. A remote diagnosis and treatment method based on a pulse simulator according to claim 1, characterized in that: Set the remote diagnosis and treatment threshold. When the remote diagnosis and treatment value is greater than or equal to the remote diagnosis and treatment threshold, mark the patient as a pulse diagnosis and treatment patient, obtain the basic patient information of the pulse diagnosis and treatment patient and the pulse signal of the pulse simulator, and register the pulse diagnosis and treatment patient to the remote diagnosis and treatment doctor's terminal. When the remote diagnosis and treatment value is less than the remote diagnosis and treatment threshold, no action will be taken.

4. A remote diagnosis and treatment system based on a pulse simulator, applied to a remote diagnosis and treatment method based on a pulse simulator as claimed in any one of claims 1 to 3, characterized in that: It includes patient pulse acquisition module, pulse parameter simulation setting module, pulse dimension diagnosis module, and pulse remote diagnosis and treatment module; The patient pulse acquisition module is used to acquire the patient's pulse signal; The pulse parameter simulation setting module is used to periodically obtain simulation parameters of various types of pulse parameters of the patient; After obtaining the simulation parameters of each type of pulse parameter of the patient, the pulse dimension diagnosis module inputs the simulation parameters of each type of pulse parameter into the pulse simulator, and sets the diagnosis order of the patient's pulse dimension data, generates a dimension diagnosis record of the pulse dimension data after diagnosing each pulse dimension data, and then continues to diagnose the next pulse dimension data according to the diagnosis order of the pulse dimension data; Whenever a dimensional diagnosis record is generated, the pulse remote diagnosis and treatment module obtains the patient's remote diagnosis and treatment value, and based on the comparison result between the remote diagnosis and treatment value and its threshold, determines whether to mark the patient as a pulse diagnosis and treatment patient. When the patient is marked as a pulse diagnosis and treatment patient, the pulse diagnosis and treatment patient is registered to the remote diagnosis and treatment doctor's terminal.

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