Personalized scheme customization management system for chronic obstructive pulmonary disease

Abstract

The invention relates to the technical field of rehabilitation evaluation of patients with chronic obstructive pulmonary disease, in particular to a personalized scheme customization management system for chronic obstructive pulmonary disease. The method comprises the following steps: acquiring physiological parameters of a chronic obstructive pulmonary disease patient, generating a lung function index MZF of the chronic obstructive pulmonary disease patient based on the physiological parameters of the chronic obstructive pulmonary disease patient, and grading the lung function of the chronic obstructive pulmonary disease patient based on the lung function index MZF of the chronic obstructive pulmonary disease patient to obtain a lung function grading result of the chronic obstructive pulmonary disease patient; planning a rehabilitation training plan of the chronic obstructive pulmonary disease patient based on the lung function grading result of the chronic obstructive pulmonary disease patient; acquiring a to-be-evaluated video of the chronic obstructive pulmonary disease patient practicing by imitating the rehabilitation training video, evaluating the to-be-evaluated video to obtain an evaluation result, and feeding back the evaluation result to the chronic obstructive pulmonary disease patient. According to the physiological parameters of the chronic obstructive pulmonary disease patient, the lung function evaluation of the chronic obstructive pulmonary disease patient can be realized, the classification evaluation of the lung function of the chronic obstructive pulmonary disease patient is carried out to obtain a chronic obstructive pulmonary disease patient rehabilitation training plan, and personalized management is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of rehabilitation assessment for patients with chronic obstructive pulmonary disease (COPD), and in particular to a personalized solution customization management system for COPD. Background Art

[0002] Chronic obstructive pulmonary disease (COPD), abbreviated as COPD, is a destructive lung disease characterized by persistent airflow limitation. Its high morbidity, high disability rate, high mortality rate and high economic burden have become an international consensus. Since most COPD patients have obvious symptoms such as dyspnea and wheezing, its treatment and management become a complex and long-term process. Therefore, the development of a personalized solution customization management system for COPD is of great significance for improving the quality of life of patients and delaying the progression of the disease.

[0003] In the prior art, the Chinese patent application with publication number CN109801715A discloses an ICF assessment system for COPD respiratory rehabilitation based on the Internet of Things. The system provides professional health guidance for COPD patients through data collection, cloud processing, ICF assessment and other steps. However, the system does not implement a grading assessment of the lung function of COPD patients, and thus lacks an effect evaluation of the rehabilitation training videos of COPD patients.

[0004] Therefore, there is an urgent need for a personalized solution customization management system for COPD to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a personalized solution customization management system for COPD: to solve the technical problem that the prior art has not achieved the grading assessment of lung function of COPD patients, and further lacks the effectiveness evaluation of rehabilitation training videos of COPD patients, and cannot form a complete closed loop from medical consultation to home rehabilitation treatment management.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A personalized solution customization management system for COPD, which includes a physiological parameter acquisition module, a COPD patient lung function assessment module, a rehabilitation training planning module, and a rehabilitation training assessment module;

[0008] The physiological parameter acquisition module is used to obtain the physiological parameters of COPD patients, wherein the physiological parameters include basic parameters of COPD patients, blood parameters of COPD patients and lung function index parameters;

[0009] The COPD patient lung function assessment module is used to generate a COPD patient lung function index MZF based on the COPD patient's physiological parameters, and to grade the COPD patient's lung function based on the COPD patient lung function index MZF to obtain a COPD patient lung function grading result;

[0010] The rehabilitation training planning module is used to plan the rehabilitation training plan for COPD patients based on the lung function classification results of COPD patients, wherein the rehabilitation training plan includes rehabilitation training videos;

[0011] The rehabilitation training evaluation module is used to obtain videos to be evaluated in which COPD patients imitate rehabilitation training videos to practice, evaluate the videos to be evaluated to obtain evaluation results, and feed back the evaluation results to the COPD patients.

[0012] Furthermore, generating the COPD patient lung function index MZF based on the COPD patient's physiological parameters specifically includes the following process:

[0013] The first coefficient of lung function JC of COPD patients is calculated based on the basic parameters of COPD patients;

[0014] The second coefficient XY of lung function of COPD patients is calculated based on the blood parameters of COPD patients;

[0015] The third coefficient of lung function FG of COPD patients is calculated based on the lung function index parameters;

[0016] Substitute the first coefficient of lung function JC of COPD patients, the second coefficient of lung function XY of COPD patients and the third coefficient of lung function FG of COPD patients into the correlation formula of lung function index MZF of COPD patients to calculate the lung function index MZF of COPD patients.

[0017] Furthermore, the first coefficient JC of the lung function of COPD patients is calculated based on the basic parameters of COPD patients, which specifically includes the following process:

[0018] Acquire lung function evaluation indicators of COPD patients, wherein the lung function evaluation indicators of COPD patients are stored in the system;

[0019] Based on the physiological parameters of the COPD patients, basic parameters of the COPD patients are obtained, wherein the basic parameters of the COPD patients include the age, weight and height of the COPD patients;

[0020] The ratio A of the age of the COPD patient to the preset age index of the COPD patient is calculated, wherein the preset age index of the COPD patient is obtained by querying the lung function evaluation index of the COPD patient;

[0021] The ratio B of the COPD patient's weight to the preset COPD patient's weight index is calculated, wherein the preset COPD patient's weight index is obtained by querying the COPD patient's lung function evaluation index;

[0022] Calculate the ratio C of the height of the COPD patient to the height index of the preset COPD patient, wherein the height index of the preset COPD patient is obtained by querying the lung function evaluation index of the COPD patient;

[0023] Substitute the age NJ, weight TZ, height SG, proportion A, proportion B and proportion C of COPD patients into the calculation formula of the first lung function coefficient of COPD patients to obtain the first lung function coefficient JC of COPD patients. The calculation formula is as follows:

[0024]

[0025] Among them, E is the number of years the COPD patient has been suffering from the disease.

[0026] Furthermore, the second coefficient XY of the lung function of the COPD patient is calculated based on the blood parameters of the COPD patient and specifically includes the following process:

[0027] Obtain the blood CRP concentration G of COPD patients based on the blood parameters of COPD patients;

[0028] Obtain the WBC count of the COPD patient based on the blood parameters of the COPD patient, and determine whether the WBC count exceeds the preset count. If so, the blood white blood cell index R is 1, otherwise the blood white blood cell index R is 0;

[0029] Substitute the blood CRP concentration G and the blood leukocyte index R into the calculation formula of the second coefficient of lung function of patients with COPD to obtain the second coefficient of lung function of patients with COPD XY. The calculation formula is as follows:

[0030] XY=k1×e R +k2×G;

[0031] Among them, k1 and k2 are weight coefficients, which are determined by the effects of blood CRP and blood white blood cells on the lung function of patients with COPD respectively. The system sets k1 to 0.6, k2 to 0.4, and e to 2.72.

[0032] Furthermore, the third coefficient FG of the lung function of COPD patients is calculated based on the lung function index parameters, which specifically includes the following process:

[0033] Obtain the forced expiratory volume L1 of COPD patients based on lung function index parameters;

[0034] Obtain the forced vital capacity L2 of COPD patients based on lung function index parameters;

[0035] Calculate the sum of the forced expiratory volume L1 and the forced vital capacity L2, and record the sum as the third coefficient of lung function FG in patients with COPD.

[0036] Further, the first coefficient of lung function of COPD patients JC, the second coefficient of lung function of COPD patients XY and the third coefficient of lung function of COPD patients FG are substituted into the COPD patients' lung function index MZF association formula to calculate the COPD patients' lung function index MZF, which specifically includes the following process:

[0037] The correlation formula of MZF for COPD patients’ lung function index is as follows:

[0038]

[0039] Furthermore, the lung function of COPD patients is graded based on the COPD patient lung function index MZF, and obtaining the COPD patient lung function grading result specifically includes the following process:

[0040] Load the COPD patient's lung function index threshold value, and determine whether the COPD patient's lung function index MZF exceeds the COPD patient's lung function index threshold value. If so, the COPD patient's lung function is at a severe damage level; if not, the COPD patient's lung function is at a mild loss level.

[0041] Furthermore, planning a rehabilitation training program for COPD patients based on the results of their lung function classification specifically includes the following processes:

[0042] For severe injury levels: Analyze the load of COPD patients based on their historical exercise history and formulate corresponding rehabilitation training plans accordingly; select appropriate training intensity and duration for COPD patients with different conditions, provide personalized prescriptions, set rehabilitation training plans of different intensities and frequencies, and continuously monitor and adjust the intensity and time interval in the middle and late stages of intervention, gradually transition to an enhanced exercise mode, so as to improve exercise capacity, improve clinical symptoms and reduce complications;

[0043] For mild injury level: the strengthening exercise mode of the rehabilitation training plan is applicable; among them, the rehabilitation training plan includes the Ba Duan Jin rehabilitation video.

[0044] Furthermore, obtaining a video to be evaluated of a COPD patient practicing according to a rehabilitation training video, and evaluating the video to be evaluated to obtain an evaluation result specifically includes the following process:

[0045] Based on the OpenPose convolutional neural network, the human posture frames in the video to be evaluated are feature extracted, and the extracted posture features are input into the dynamic time warping algorithm. The similarity is calculated with the standard posture to obtain the similarity, and whether the human posture frame is qualified is judged based on the similarity.

[0046] Furthermore, the extracted posture features are input into the dynamic time warping algorithm to calculate the similarity with the standard posture. The specific process of obtaining the similarity includes the following steps:

[0047] Assume that the change sequence of the coordinates of the posture factors in the standard action is S = (s1, s2...s n), where n is the sequence number, the posture factors include the wrist, knee, elbow and ankle, and the change sequence of the patient's posture factor coordinates is obtained according to the posture characteristics T = (t1, t2...t m ), m is the sequence number;

[0048] Construct a loss matrix C with n rows and m columns p (S, T):

[0049] Among them, C(s ik , t jk ) represents the s corresponding to the sequence S at the i-th moment ik t corresponding to the sequence T at the jth moment jk The Euclidean distance between them, the value of k is 1, 2...K, where K is the loss matrix C p The number of regular paths in (S, T);

[0050] C p The value of (S, T) is taken as the similarity.

[0051] Compared with the existing solutions, the present invention achieves the following beneficial effects:

[0052] On the one hand, it provides personalized program customization: according to the physiological parameters of COPD patients, it can realize the lung function evaluation of COPD patients, and the lung function of COPD patients can be graded and the results of COPD patients' rehabilitation training plan can be formulated to achieve personalized management.

[0053] On the other hand, the present invention can monitor the patient's rehabilitation training video in real time through intelligent hardware devices, forming a complete closed loop from medical consultation to home rehabilitation treatment management. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0055] Figure 1 This is a system block diagram of a personalized solution customization management system for chronic obstructive pulmonary disease according to an embodiment of the present invention;

[0056] Figure 2 This is a workflow diagram of a personalized solution customization management system for chronic obstructive pulmonary disease according to an embodiment of the present invention;

[0057] Figure 3 This is a workflow diagram of another personalized solution customization management system for COPD according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] 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.

[0059] In addition, the described features, structures or characteristics may be combined in one or more example embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the example embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, steps, etc. may be adopted. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0060] This embodiment provides a personalized solution customization management system for COPD. Figure 1 is a system block diagram of a personalized solution customization management system for COPD according to an embodiment of the present invention. Figure 1 As shown, the system includes a physiological parameter acquisition module, a COPD patient lung function assessment module, a rehabilitation training planning module and a rehabilitation training assessment module;

[0061] The physiological parameter acquisition module is used to obtain the physiological parameters of COPD patients, wherein the physiological parameters include basic parameters of COPD patients, blood parameters of COPD patients and lung function index parameters;

[0062] The COPD patient lung function assessment module is used to generate a COPD patient lung function index MZF based on the COPD patient's physiological parameters, and to grade the COPD patient's lung function based on the COPD patient lung function index MZF to obtain a COPD patient lung function grading result;

[0063] The rehabilitation training planning module is used to plan the rehabilitation training plan for COPD patients based on the lung function classification results of COPD patients, wherein the rehabilitation training plan includes rehabilitation training videos;

[0064] The rehabilitation training evaluation module is used to obtain videos to be evaluated in which COPD patients imitate rehabilitation training videos to practice, evaluate the videos to be evaluated to obtain evaluation results, and feed back the evaluation results to the COPD patients.

[0065] In summary, the present invention can obtain the physiological parameters of COPD patients, generate the COPD patient lung function index MZF based on the COPD patient's physiological parameters, grade the COPD patient's lung function based on the COPD patient's lung function index MZF, and obtain the COPD patient lung function grading result; plan the COPD patient rehabilitation training plan based on the COPD patient lung function grading result; obtain the COPD patient's to-be-evaluated video practicing by imitating the rehabilitation training video, evaluate the to-be-evaluated video to obtain the evaluation result, and feed back the evaluation result to the COPD patient. The present invention can evaluate the lung function of COPD patients according to the COPD patient's physiological parameters, and grade the lung function of COPD patients to evaluate the results of the COPD patient rehabilitation training plan, thereby realizing personalized management. The present invention can monitor the patient's rehabilitation training video in real time through intelligent hardware equipment, forming a complete closed loop from consultation to home rehabilitation treatment management.

[0066] It is worth mentioning that the physiological parameter acquisition module, the COPD patient lung function assessment module, the rehabilitation training planning module and the rehabilitation training assessment module can all achieve two-to-two communication and complete data transmission.

[0067] In some embodiments, Figure 2 is a workflow diagram of a personalized solution customization management system for COPD according to an embodiment of the present invention, such as Figure 2 As shown, generating the COPD patient lung function index MZF based on the COPD patient's physiological parameters specifically includes the following steps:

[0068] Step S201: Calculating the first lung function coefficient JC of the COPD patient based on the basic parameters of the COPD patient;

[0069] Step S202: Calculating a second lung function coefficient XY of the COPD patient based on the COPD patient's blood parameters;

[0070] Step S203: Calculating the third lung function coefficient FG of the COPD patient based on the lung function index parameter;

[0071] Step S204: Substitute the first lung function coefficient JC of the COPD patient, the second lung function coefficient XY of the COPD patient and the third lung function coefficient FG of the COPD patient into the COPD patient lung function index MZF association formula to calculate the COPD patient lung function index MZF.

[0072] In some embodiments, calculating the first lung function coefficient JC of the COPD patient based on the basic parameters of the COPD patient specifically includes the following process:

[0073] Acquire lung function evaluation indicators of COPD patients, wherein the lung function evaluation indicators of COPD patients are stored in the system;

[0074] Based on the physiological parameters of the COPD patients, basic parameters of the COPD patients are obtained, wherein the basic parameters of the COPD patients include the age, weight and height of the COPD patients;

[0075] The ratio A of the age of the COPD patient to the preset age index of the COPD patient is calculated, wherein the preset age index of the COPD patient is obtained by querying the lung function evaluation index of the COPD patient;

[0076] The ratio B of the COPD patient's weight to the preset COPD patient's weight index is calculated, wherein the preset COPD patient's weight index is obtained by querying the COPD patient's lung function evaluation index;

[0077] Calculate the ratio C of the height of the COPD patient to the height index of the preset COPD patient, wherein the height index of the preset COPD patient is obtained by querying the lung function evaluation index of the COPD patient;

[0078] Substitute the age NJ, weight TZ, height SG, proportion A, proportion B and proportion C of COPD patients into the calculation formula of the first lung function coefficient of COPD patients to obtain the first lung function coefficient JC of COPD patients. The calculation formula is as follows:

[0079]

[0080] Among them, E is the number of years that COPD patients have suffered from the disease.

[0081] In some embodiments, calculating the second coefficient XY of lung function of COPD patients based on the blood parameters of COPD patients specifically includes the following process:

[0082] Obtain the blood CRP concentration G of COPD patients based on the blood parameters of COPD patients;

[0083] Obtain the WBC count of the COPD patient based on the blood parameters of the COPD patient, and determine whether the WBC count exceeds the preset count. If so, the blood white blood cell index R is 1, otherwise the blood white blood cell index R is 0;

[0084] Substitute the blood CRP concentration G and the blood leukocyte index R into the calculation formula of the second coefficient of lung function of patients with COPD to obtain the second coefficient of lung function of patients with COPD XY. The calculation formula is as follows:

[0085] XY=k1×e R +k2×G;

[0086] Among them, k1 and k2 are weight coefficients, which are determined by the effects of blood CRP and blood white blood cells on the lung function of patients with COPD respectively. The system sets k1 to 0.6, k2 to 0.4, and e to 2.72.

[0087] In some embodiments, Figure 3 is a workflow diagram of another COPD-oriented personalized solution customization management system according to an embodiment of the present invention. Figure 3 As shown, the third coefficient FG of the lung function of COPD patients is calculated based on the lung function index parameters, which specifically includes the following steps:

[0088] Step S301: obtaining the forced expiratory volume L1 of a COPD patient based on the lung function index parameter;

[0089] Step S302: obtaining the forced vital capacity L2 of the COPD patient based on the lung function index parameter;

[0090] Step S303: Calculate the sum of the forced expiratory volume L1 and the forced vital capacity L2, and record the sum as the third coefficient of lung function FG of the COPD patient.

[0091] In some embodiments, the COPD patient's lung function index MZF is calculated by substituting the COPD patient's lung function first coefficient JC, the COPD patient's lung function second coefficient XY, and the COPD patient's lung function third coefficient FG into the COPD patient's lung function index MZF correlation formula, and specifically includes the following process:

[0092] The correlation formula of MZF for COPD patients’ lung function index is as follows:

[0093]

[0094] In some embodiments, the lung function of COPD patients is graded based on the COPD patient lung function index MZF, and obtaining the COPD patient lung function grading result specifically includes the following process:

[0095] The COPD patient lung function index threshold is loaded to determine whether the COPD patient lung function index MZF exceeds the COPD patient lung function index threshold. If so, the COPD patient lung function is at a severe damage level; if not, the COPD patient lung function is at a mild loss level. The COPD patient lung function index threshold is set by the system and stored in the system.

[0096] In some embodiments, planning a rehabilitation training program for COPD patients based on the results of the COPD patients' lung function classification specifically includes the following process:

[0097] For severe injury levels: Analyze the load of COPD patients based on their historical exercise history and formulate corresponding rehabilitation training plans accordingly; select appropriate training intensity and duration for COPD patients with different conditions, provide personalized prescriptions, set rehabilitation training plans of different intensities and frequencies, and continuously monitor and adjust the intensity and time interval in the middle and late stages of intervention, gradually transition to an enhanced exercise mode, so as to improve exercise capacity, improve clinical symptoms and reduce complications;

[0098] For mild injury level: the strengthening exercise mode of the rehabilitation training plan is applicable; wherein the rehabilitation training plan includes the Ba Duan Jin rehabilitation video. The strengthening exercise mode is a pre-set Ba Duan Jin video training mode.

[0099] Obtaining a video to be evaluated of a COPD patient imitating a rehabilitation training video and evaluating the video to be evaluated to obtain an evaluation result specifically includes the following process:

[0100] In some embodiments, feature extraction is performed on human posture frames in the video to be evaluated based on the OpenPose convolutional neural network, and the extracted posture features are input into a dynamic time warping algorithm, and similarity is calculated with the standard posture to obtain the similarity, and whether the human posture frame is qualified is determined based on the similarity.

[0101] Specifically, OpenPose is an open source real-time multi-person pose estimation library developed by Carnegie Mellon University (CMU) in the United States. It uses convolutional neural networks (CNN) to detect key points of the human body in input images or videos to estimate the human body's pose. OpenPose can detect and track the poses of multiple people at the same time, and is suitable for a variety of applications such as human-computer interaction, motion analysis, motion capture, virtual reality, etc.

[0102] Furthermore, the extracted posture features are input into the dynamic time warping algorithm to calculate the similarity with the standard posture. The specific process of obtaining the similarity includes the following steps:

[0103] Assume that the change sequence of the coordinates of the posture factors in the standard action is S = (s1, s2...s n ), where n is the sequence number, the posture factors include the wrist, knee, elbow and ankle, and the change sequence of the patient's posture factor coordinates is obtained according to the posture characteristics T = (t1, t2...t m ), m is the sequence number;

[0104] Construct a loss matrix C with n rows and m columns p (S, T):

[0105] Among them, C(s ik , t jk ) represents the s corresponding to the sequence S at the i-th moment ik t corresponding to the sequence T at the jth moment jk The Euclidean distance between them, the value of k is 1, 2...K, where K is the loss matrix C p The number of regular paths in (S, T);

[0106] C p The value of (S, T) is taken as the similarity.

[0107] If the similarity is greater than a preset similarity threshold, it indicates that the posture movement in the human posture frame is qualified. If not, it indicates that the posture movement in the human posture frame is unqualified. The video to be evaluated is evaluated according to the above method, and the evaluation result is fed back to the patient.

[0108] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by 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 process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0109] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0110] 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 aforementioned method embodiments and will not be repeated here.

[0111] In the 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 only schematic. For example, the division of the units is only some logical function divisions. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0112] 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, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0113] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A personalized solution customization management system for COPD, characterized by: The system includes a physiological parameter acquisition module, a COPD patient lung function assessment module, a rehabilitation training planning module, and a rehabilitation training assessment module; The physiological parameter acquisition module is used to obtain the physiological parameters of COPD patients, wherein the physiological parameters include basic parameters of COPD patients, blood parameters of COPD patients and lung function index parameters; The COPD patient lung function assessment module is used to generate a COPD patient lung function index MZF based on the COPD patient's physiological parameters, and to grade the COPD patient's lung function based on the COPD patient lung function index MZF to obtain a COPD patient lung function grading result; The rehabilitation training planning module is used to plan the rehabilitation training plan for COPD patients based on the lung function classification results of COPD patients, wherein the rehabilitation training plan includes rehabilitation training videos; The rehabilitation training evaluation module is used to obtain videos to be evaluated in which COPD patients imitate rehabilitation training videos to practice, evaluate the videos to be evaluated to obtain evaluation results, and feed back the evaluation results to the COPD patients.

2. The personalized solution customization management system for COPD according to claim 1 is characterized in that: Generating the COPD patient lung function index MZF based on the physiological parameters of COPD patients specifically includes the following processes: The first coefficient of lung function JC of COPD patients is calculated based on the basic parameters of COPD patients; The second coefficient XY of lung function of COPD patients is calculated based on the blood parameters of COPD patients; The third coefficient of lung function FG of COPD patients is calculated based on the lung function index parameters; Substitute the first coefficient of lung function JC of COPD patients, the second coefficient of lung function XY of COPD patients and the third coefficient of lung function FG of COPD patients into the correlation formula of lung function index MZF of COPD patients to calculate the lung function index MZF of COPD patients.

3. The personalized solution customization management system for COPD according to claim 2 is characterized in that: The calculation of the first coefficient of lung function JC of COPD patients based on the basic parameters of COPD patients specifically includes the following process: Acquire lung function evaluation indicators of COPD patients, wherein the lung function evaluation indicators of COPD patients are stored in the system; Based on the physiological parameters of the COPD patients, basic parameters of the COPD patients are obtained, wherein the basic parameters of the COPD patients include the age, weight and height of the COPD patients; The ratio A of the age of the COPD patient to the preset age index of the COPD patient is calculated, wherein the preset age index of the COPD patient is obtained by querying the lung function evaluation index of the COPD patient; The ratio B of the COPD patient's weight to the preset COPD patient's weight index is calculated, wherein the preset COPD patient's weight index is obtained by querying the COPD patient's lung function evaluation index; Calculate the ratio C of the height of the COPD patient to the height index of the preset COPD patient, wherein the height index of the preset COPD patient is obtained by querying the lung function evaluation index of the COPD patient; Substitute the age NJ, weight TZ, height SG, proportion A, proportion B and proportion C of COPD patients into the calculation formula of the first lung function coefficient of COPD patients to obtain the first lung function coefficient JC of COPD patients. The calculation formula is as follows: Among them, E is the number of years the COPD patient has been suffering from the disease.

4. The personalized solution customization management system for COPD according to claim 2 is characterized in that: The second coefficient XY of lung function of COPD patients is calculated based on the blood parameters of COPD patients. The process includes: Obtain the blood CRP concentration G of COPD patients based on the blood parameters of COPD patients; Obtain the WBC count of the COPD patient based on the blood parameters of the COPD patient, and determine whether the WBC count exceeds the preset count. If so, the blood white blood cell index R is 1, otherwise the blood white blood cell index R is 0; Substitute the blood CRP concentration G and the blood leukocyte index R into the calculation formula of the second coefficient of lung function of patients with COPD to obtain the second coefficient of lung function of patients with COPD XY. The calculation formula is as follows: XY=k1×e R +k2×G; Among them, k1 and k2 are weight coefficients, which are determined by the effects of blood CRP and blood white blood cells on the lung function of patients with COPD respectively. The system sets k1 to 0.6, k2 to 0.4, and e to 2.

72.

5. The personalized solution customization management system for COPD according to claim 2 is characterized in that: The third coefficient of lung function FG of COPD patients is calculated based on the lung function index parameters, which specifically includes the following process: Obtain the forced expiratory volume L1 of COPD patients based on lung function index parameters; Obtain the forced vital capacity L2 of COPD patients based on lung function index parameters; Calculate the sum of the forced expiratory volume L1 and the forced vital capacity L2, and record the sum as the third coefficient of lung function FG in patients with COPD.

6. The personalized solution customization management system for COPD according to claim 2 is characterized in that: Substituting the first coefficient of lung function JC, the second coefficient of lung function XY and the third coefficient of lung function FG into the correlation formula of lung function index MZF of COPD patients to calculate the lung function index MZF of COPD patients specifically includes the following process: The correlation formula of MZF for COPD patients’ lung function index is as follows:

7. The personalized solution customization management system for COPD according to claim 1 is characterized in that: Based on the COPD patient lung function index MZF, the COPD patient lung function is graded, and the COPD patient lung function grading results specifically include the following process: Load the COPD patient's lung function index threshold value, and determine whether the COPD patient's lung function index MZF exceeds the COPD patient's lung function index threshold value. If so, the COPD patient's lung function is at a severe damage level; if not, the COPD patient's lung function is at a mild loss level.

8. The personalized solution customization management system for COPD according to claim 1, characterized in that: Planning a rehabilitation training program for COPD patients based on the results of COPD patients' lung function classification specifically includes the following processes: For severe injury levels: Analyze the load of COPD patients based on their historical exercise history and formulate corresponding rehabilitation training plans accordingly; select appropriate training intensity and duration for COPD patients with different conditions, provide personalized prescriptions, set rehabilitation training plans of different intensities and frequencies, and continuously monitor and adjust the intensity and time interval in the middle and late stages of intervention, gradually transition to an enhanced exercise mode, so as to improve exercise capacity, improve clinical symptoms and reduce complications; For mild injury level: the strengthening exercise mode of the rehabilitation training plan is applicable; among them, the rehabilitation training plan includes the Ba Duan Jin rehabilitation video.

9. The personalized solution customization management system for COPD according to claim 1, characterized in that: Obtaining a video to be evaluated of a COPD patient imitating a rehabilitation training video and evaluating the video to be evaluated to obtain an evaluation result specifically includes the following process: Based on the OpenPose convolutional neural network, the human posture frames in the video to be evaluated are feature extracted, and the extracted posture features are input into the dynamic time warping algorithm. The similarity is calculated with the standard posture to obtain the similarity, and whether the human posture frame is qualified is judged based on the similarity.

10. The personalized solution customization management system for COPD according to claim 9, characterized in that: The extracted posture features are input into the dynamic time warping algorithm, and the similarity is calculated with the standard posture. The specific process of obtaining the similarity includes the following: Assume that the change sequence of the coordinates of the posture factors in the standard action is S = (s1, s2...s n ), where n is the sequence number, the posture factors include the wrist, knee, elbow and ankle, and the change sequence of the patient's posture factor coordinates is obtained according to the posture characteristics T = (t1, t2...t m ), m is the sequence number; Construct a loss matrix C with n rows and m columns p (S, T): Among them, C(s ik , t jk ) represents the s corresponding to the sequence S at the i-th moment ik t corresponding to the sequence T at the jth moment jk The Euclidean distance between them, the value of k is 1, 2...K, where K is the loss matrix C p The number of regular paths in (S, T); C p The value of (S, T) is taken as the similarity.

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